52nd week of 2021 patent applcation highlights part 59 |
Patent application number | Title | Published |
20210406673 | INTERFACE TRANSLATION USING ONE OR MORE NEURAL NETWORKS - Apparatuses, systems, and techniques are presented to generate one or more interfaces. In at least one embodiment, one or more neural networks are used to generate one or more second graphical user interfaces based, at least in part, on one or more functional features of one or more first graphical user interfaces | 2021-12-30 |
20210406674 | System and Method for Sensor Fusion System Having Distributed Convolutional Neural Network - An early fusion network is provided that reduces network load and enables easier design of specialized ASIC edge processors through performing a portion of convolutional neural network layers at distributed edge and data-network processors prior to transmitting data to a centralized processor for fully-connected/deconvolutional neural networking processing. Embodiments can provide convolution and downsampling layer processing in association with the digital signal processors associated with edge sensors. Once the raw data is reduced to smaller feature maps through the convolution-downsampling process, this reduced data is transmitted to a central processor for further processing such as regression, classification, and segmentation, along with feature combination of the data from the sensors. In some embodiments, feature combination can be distributed to gateway or switch nodes closer to the edge sensors, thereby further reducing the data transferred to the central node and reducing the amount of computation performed there. | 2021-12-30 |
20210406675 | METHOD FOR FORECASTING HEALTH STATUS OF DISTRIBUTED NETWORKS BY ARTIFICIAL NEURAL NETWORKS - The present invention relates to a method for forecasting health status of a distributed network by an artificial neural network comprising the phase of identifying one or more sites, one or more assets of the sides and the links between the identified assets in said distributed network, comprising the phase of evaluating the actual health status of each of the identified assets, the phase of evaluating the actual health status of each of said identified sites and the phase of forecasting, by the artificial neural network, the subsequent health status of each of the identified sites according to a forecasting function based on a set of values comprising the actual asset health status rank, the actual asset infection risk, the actual asset infection factor, the actual site health status rank and the actual site infection risk. | 2021-12-30 |
20210406676 | VARIABLE INPUT SIZE TECHNIQUES FOR NEURAL NETWORKS - A neural network, trained on a plurality of random size data samples, can receive a plurality of inference data samples including samples of different sizes. The neural network can generate feature maps of the plurality of inference data samples. Pooling can be utilized to generate feature maps having a fixed size. The fixed size feature maps can be utilized to generate an indication of a class for each of the plurality of inference data samples. | 2021-12-30 |
20210406677 | Deep Neural Network Processing for a User Equipment-Coordination Set - Techniques and apparatuses are described for deep neural network (DNN) processing for a user equipment-coordination set (UECS). A network entity selects ( | 2021-12-30 |
20210406678 | PREDICTING FAILURES WITH CODE FLOW TENSORS - Techniques for predicting states may include: receiving data sets of counter values, wherein each counter values denotes a number of times a particular code flow point associated with the counter value is executed at runtime during a specified time period; receiving images generated from the data sets; labeling each of the images with state information, wherein first state information associated with a first image indicates that the first image is associated with a first error state of a system or an application; training a neural network using the images to recognize the first state; receiving a next image generated from another data set; and predicting, by the neural network and in accordance with the next image, whether the system or the application is expected to transition into the first state. In at least one embodiment, the foregoing processing may optionally use matrices generated from the data sets rather than images. | 2021-12-30 |
20210406679 | MULTI-RESOLUTION IMAGE PATCHES FOR PREDICTING AUTONOMOUS NAVIGATION PATHS - In examples, image data representative of an image of a field of view of at least one sensor may be received. Source areas may be defined that correspond to a region of the image. Areas and/or dimensions of at least some of the source areas may decrease along at least one direction relative to a perspective of the at least one sensor. A downsampled version of the region (e.g., a downsampled image or feature map of a neural network) may be generated from the source areas based at least in part on mapping the source areas to cells of the downsampled version of the region. Resolutions of the region that are captured by the cells may correspond to the areas of the source areas, such that certain portions of the region (e.g., portions at a far distance from the sensor) retain higher resolution than others. | 2021-12-30 |
20210406680 | PAIRWISE RANKING USING NEURAL NETWORKS - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network used to generate a ranking score for a network input. One of the methods includes generating training data and training the neural network on the training data. The training data includes a plurality of training pairs. The generating comprising: obtaining data indicating that a plurality of training network inputs were displayed in a user interface according to a presentation order, obtaining data indicating that a first training network input of the plurality of training network inputs has a positive label, determining that a second training network input of the plurality of training network inputs (i) has a negative label and (ii) is higher than the first training network input in the presentation order, and generating a training pair that includes the first training network input and the second training network input. | 2021-12-30 |
20210406681 | LEARNING LOSS FUNCTIONS USING DEEP LEARNING NETWORKS - Techniques are provided for learning loss functions using DL networks and integrating these loss functions into DL based image transformation architectures. In one embodiment, a method is provided that comprising facilitating training, by a system operatively coupled to a processor, a first deep learning network to predict a loss function metric value of a loss function. The method further comprises employing, by the system, the first deep learning network to predict the loss function metric value in association with training a second deep learning network that to perform a defined deep learning task. In various embodiments, the loss function comprises a computationally complex loss function that is not easily implementable in existing deep learning packages, such as a non-differentiable loss function, a feature similarity index match (FSIM) loss function, a system transfer function, a visual information fidelity (VIF) loss function and the like. | 2021-12-30 |
20210406682 | QUANTIZATION OF NEURAL NETWORK MODELS USING DATA AUGMENTATION - A neural network is trained at a first precision using a training dataset. The neural network is then calibrated using an augmented calibration dataset that includes a first dataset and one or more second datasets produced by modifying the first dataset. A range of values of activations of nodes in the neural network at the first precision is determined based on inputs to the neural network from the augmented calibration dataset. The activations of the nodes are then quantized to a second precision based on the range of values of the activations of the nodes at the first precision. The first precision is higher than the second precision. For example, in some cases the first precision is a 32-bit floating point precision and the second precision is an 8-bit integer precision. | 2021-12-30 |
20210406683 | LEARNING METHOD AND INFORMATION PROCESSING APPARATUS - A process includes starting a learning process for building a model including multiple layers each including a parameter. The learning process executes iterations, each including calculating output error of the model using training data and updating the parameter value based on the output error. The process also includes selecting two or more candidate layers representing candidates for layers, where the updating is to be suppressed, based on results of a first iteration of the learning process. The process also includes calculating, based on the number of iterations executed up to the first iteration, a ratio value which becomes larger when the number of iterations executed is greater, and determining, amongst the candidate layers, one or more layers, where the updating is to be suppressed at a second iteration following the first iteration. The number of one or more layers is determined according to the ratio value. | 2021-12-30 |
20210406684 | METHOD FOR TRAINING A NEURAL NETWORK - A computer-implemented method for training a neural network, which, in particular, is configured to classify physical measuring variables. The neural network is trained with the aid of a training data set. Pairs including an input signal and an associated desired output signal are drawn from the training data set for training. An adaptation of parameters of the neural network occurs as a function of an output signal of the neural network, when the input signal is supplied, and as a function of the desired output signal. The drawing of pairs always takes place from the entire training data set. | 2021-12-30 |
20210406685 | ARTIFICIAL INTELLIGENCE FOR KEYWORD RECOMMENDATION - Artificial intelligence for keyword recommendations. In an embodiment, raw keyword data are received. The raw keyword data comprise keyword activity records that each comprises a uniform resource locator (URL) for an online resource and metadata for the online resource. Arrays of keywords are extracted from the keyword activity records, with each array of keywords associated with the URL in the keyword activity record from which the array of keywords was extracted. User-specified keyword(s) are received, and a subset of the arrays of keywords that match at least one of the user-specified keyword(s) is identified. A training dataset is generated from the subset, and used to train a machine-learning model to output recommended keywords based on an input keyword. | 2021-12-30 |
20210406686 | METHOD AND SYSTEM FOR BALANCED-WEIGHT SPARSE CONVOLUTION PROCESSING - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for balanced-weight sparse convolution processing. An exemplary method comprises: obtaining an input tensor and a plurality of filters at a layer within a neural network; segmenting the input tensor into a plurality of sub-tensors; dividing a channel dimension of each of the plurality of filters into a plurality of channel groups; pruning each of the plurality of filters so that each of the plurality of channel groups of each filter comprises a same number of non-zero weights; segmenting each of the plurality of filters into a plurality of the sub-filters according to the plurality of channel groups; and assigning the plurality of sub-tensors and the plurality of sub-filters to a plurality of processors for parallel convolution processing. | 2021-12-30 |
20210406687 | METHOD FOR PREDICTING ATTRIBUTE OF TARGET OBJECT BASED ON MACHINE LEARNING AND RELATED DEVICE - This application discloses a method for predicting an attribute of a target object based on machine learning and a related device, which belong to the field of data prediction technologies. According to the method, a global feature of the target object is determined based on a rule feature representing historical and future change rules of a detection feature, and the global feature is refined to obtain at least one local feature of the target object, so that the refined local feature can better reflect the feature of the target object, and the attribute of the target object is further predicted based on the local feature, thereby improving the precision of the predicted attribute. When the attribute of the target object is a predicted diagnosis result, the precision of the predicted diagnosis result can be improved. | 2021-12-30 |
20210406688 | METHOD AND DEVICE WITH CLASSIFICATION VERIFICATION - A method and computing device with classification verification is provided. A processor-implemented method includes implementing a classification neural network to generate a classification result of data input to the classification neural network by generating, with respect to the input data, intermediate hidden values of one or more hidden layers of the classification neural network, generating the classification result of the input data based on the generated intermediate hidden values, and generating a determination of a reliability of the classification result by implementing a verification neural network, input the intermediate hidden values, to generate the determination of the reliability. | 2021-12-30 |
20210406689 | Random Action Replay for Reinforcement Learning - An artificial intelligence (AI) platform to support random action replay for natural language (NL) learning. A NL conversation is explored to train a neural network. One or more tuples are leverage for the training, with each tuple representing an input action, a vector, an output action, and a reward value. An action is sampled from the vector, with the sampling including assessment of a corresponding first gradient. The first gradient is applied to selectively adjust the neural network. As NL input is received and applied to the selectively adjusted neural network, an output corresponding to the NL input is identified and a corresponding action is executed. | 2021-12-30 |
20210406690 | EFFICIENT WEIGHT CLIPPING FOR NEURAL NETWORKS - Systems, apparatuses, and methods for implementing one-sided per-kernel clipping and weight transformation for neural networks are disclosed. Various parameters of a neural network are quantized from higher-bit representations to lower-bit representations to reduce memory utilization and power consumption. To exploit the effective range of quantized representations, positively biased weights are clipped and negated before convolution. Then, the results are rescaled back after convolution. A one-sided clipping technique is used for transforming weights to exploit the quantization range effectively, with the side chosen to be clipped being the biased side. This technique uses a global strategy for clipping without requiring skilled expertise. This approach allows the system to retain as much information as possible without losing unnecessary accuracy when quantizing parameters from higher-bit representations to lower-bit representations. | 2021-12-30 |
20210406691 | METHOD AND APPARATUS FOR MULTI-RATE NEURAL IMAGE COMPRESSION WITH MICRO-STRUCTURED MASKS - A method of multi-rate neural image compression includes selecting encoding masks, based on a hyperparameter, and performing a convolution of a first plurality of weights of a first neural network and the selected encoding masks to obtain first masked weights. The method further includes encoding an input image to obtain an encoded representation, using the first masked weights, and encoding the obtained encoded representation to obtain a compressed representation. | 2021-12-30 |
20210406692 | PARTIAL-ACTIVATION OF NEURAL NETWORK BASED ON HEAT-MAP OF NEURAL NETWORK ACTIVITY - A device, system, and method for training or prediction of a neural network. A current value may be stored for each of a plurality of synapses or filters in the neural network. A historical metric of activity may be independently determined for each individual or group of the synapses or filters during one or more past iterations. A plurality of partial activations of the neural network may be iteratively executed. Each partial-activation iteration may activate a subset of the plurality of synapses or filters in the neural network. Each individual or group of synapses or filters may be activated in a portion of a total number of iterations proportional to the historical metric of activity independently determined for that individual or group of synapses or filters. Training or prediction of the neural network may be performed based on the plurality of partial activations of the neural network. | 2021-12-30 |
20210406693 | DATA SAMPLE ANALYSIS IN A DATASET FOR A MACHINE LEARNING MODEL - A method is described for analyzing data samples of a machine learning (ML) model to determine why the ML model classified a sample like it did. Two samples are chosen for analysis. The two samples may be nearest neighbors. Samples classified as nearest neighbors are typically samples that are more similar with respect to a predetermined criterion than other samples of a set of samples. In the method, a first set of features of a first sample and a second set of features of a second sample are collected. A set of overlapping features of the first and second sets of features is determined. Then, the set of overlapping features is analyzed using a predetermined visualization technique to determine why the ML model determined the first sample to be similar to the second sample. | 2021-12-30 |
20210406694 | NEUROMORPHIC APPARATUS AND METHOD WITH NEURAL NETWORK - A processor-implemented neural network implementation method includes: learning each of first layers included in a neural network according to a first method; learning at least one second layer included in the neural network according to a second method; and generating output data from input data by using the learned first layers and the learned at least one second layer. | 2021-12-30 |
20210406695 | Systems and Methods for Training an Autoencoder Neural Network Using Sparse Data - Methods and systems are provided to prevent pathological overfitting in training autoencoder networks, by forcing the network to only model structure that is shared between different data variables and to enable an automatic search of hyperparameters in training autoencoder networks, resulting in automated discovery of optimally-trained models. The method may include training a neural network. The training may include applying a first binary mask to the set of training data to determine the training input data. The training may include processing the training input data by the neural network to produce network output data. The training may include determining one or more updates of the parameters based on a comparison of at least a portion of the network output data and a corresponding portion of the training data. The portion of the network output data and the corresponding portion of the training input data being inverts. | 2021-12-30 |
20210406696 | LEARNING-BASED SERVICE MIGRATION IN MOBILE EDGE COMPUTING - Learning-based service migration in mobile edge computing may be provided. First, a service migration policy may be created for a network that includes a plurality of edge clouds configured to provide a service to users. Next, a movement of a user receiving the service from a source edge cloud may be detected. The source edge cloud may be associated with a first area and the detected movement may be from the first area to a second area. Then, the service migration policy may be applied to determine whether to migrate the service for the user from the source edge cloud. In response to determining to migrate the service, a target edge cloud may be identified and the service for the user may be migrated from the source edge cloud to the target edge cloud. The service migration policy may then be updated based on a success of the migration. | 2021-12-30 |
20210406697 | INTERACTION DETERMINATION USING ONE OR MORE NEURAL NETWORKS - Apparatuses, systems, and techniques are presented to generate image or video content indicating possible interactions with objects in an electronic game or other presentation of content. In at least one embodiment, one or more neural networks are used to generate one or more images indicating one or more interactions between a user and one or more objects in the one or more images | 2021-12-30 |
20210406698 | IDENTIFYING ANOMALOUS ACTIVITY FROM THERMAL IMAGES - A computing system may train an autoencoder to generate a first set of codes from a first set of thermal video images of activities of a user in an environment. The activities may represent routine behaviors of the user in the environment. The computing system may use an unsupervised machine-learning algorithm to categorize the first set of codes into a set of clusters. The computing system may use the autoencoder to determine a code representative of a second set of thermal video images of an activity in the environment. Based on the code not being associated with any cluster in the set of clusters, the computing system may determine that the code is an anomalous code. The computing system may perform an alert action based on the anomalous code. | 2021-12-30 |
20210406700 | SYSTEMS AND METHODS FOR TEMPORALLY SENSITIVE CAUSAL HEURISTICS - A system for temporally sensitive causal heuristics, the system comprising a computing device includes a computing device configured to provide a plurality of constitutional events and a plurality of potential effects relating to a human subject, wherein each constitutional event of the plurality of constitutional events includes an event type, a significance level, a time of occurrence, a temporal function, and at least a potential effect of the plurality of potential effects, generate a ranking of the plurality of constitutional events as a function of the significance level, time of occurrence, and temporal effect factor of each constitutional event, receive at least a current occurrence input from the human subject, classify the at least a current occurrence input to an identified potential effect of the plurality of potential effects as a function of the ranking, and output the identified potential effect. | 2021-12-30 |
20210406701 | HYBRID MACHINE LEARNING MODEL FOR CODE CLASSIFICATION - An embodiment involves a hybrid machine learning classifier that uses a random forest of decision tree classifiers to predict a tariff code prefix, and uses a plurality of expert trees to predict a tariff code suffix from properties related to chemical components associated with the respective tariff code prefixes. The embodiment also involves: determining a proportion of a dominant chemical component in comparison to other chemical components in a new set of chemical components; calculating similarity scores for the new set of chemical components and words associated with the tariff code prefixes; generating a feature vector from the proportion and the similarity scores; and obtaining a predicted tariff code including a predicted tariff code prefix determined by applying the random forest to the feature vector, and a predicted tariff code suffix determined by traversing a particular expert tree in accordance with properties related to the new set of chemical components. | 2021-12-30 |
20210406702 | APPARATUS AND METHOD FOR FILLING A KNOWLEDGE GRAPH BY WAY OF STRATEGIC DATA SPLITS - A method for filling a knowledge graph. A first and second subset of data points are determined. A data point to which a label is assigned is associated with a cluster from among a set of clusters, depending on whether a distribution of labels from data points that are already associated with the cluster satisfies a condition. Data points that are associated with the cluster are associated with the first or second subset. Models for classification are trained depending on data points from the first subset. For at least one of the models, a value of a quality factor is determined depending on data points from the second subset. A model for classification is selected from the models depending on the value. A classification that defines a relationship, node, or type of node in the knowledge graph for the sentence is determined using the selected model. | 2021-12-30 |
20210406703 | METHOD FOR REAL-TIME ENHANCEMENT OF A PREDICTIVE ALGORITHM BY A NOVEL MEASUREMENT OF CONCEPT DRIFT USING ALGORITHMICALLY-GENERATED FEATURES - A predictive analytics system and method in the setting of multi-class classification are disclosed, for identifying systematic changes in an evaluation dataset processed by a fraud-detection model by examining the time series histories of an ensemble of entities such as accounts. The ensemble of entities is examined and processed both individually and in aggregate, via a set of features determined previously using a distinct training dataset. The specific set of features in question may be calculated from the entity's time series history, and may or may not be used by the model to perform the classification. Certain properties of the detected changes are measured and used to improve the efficacy of the predictive model. | 2021-12-30 |
20210406704 | INFORMATION DELIVERY PLATFORM - An information delivery system allows for the organization and presentation of information to users. Illustratively, aspects of the disclosure correspond to a system and method which provides for interactive information delivery, or interactive learning. More particularly, a platform is disclosed which provides an independent interactive interface for content delivery and e-learning and for creation of teaching or learning presentations. | 2021-12-30 |
20210406705 | SYSTEM AND METHOD FOR COLLECTING TRAINING DATA - A system collects training data in order to train a determination model of artificial intelligence that determines an abnormality of an industrial machine. The system includes a storage device and a processor. The storage device stores state data indicative of a state of the industrial machine acquired in time series. The processor determines an occurrence of a trigger related to an occurrence of the abnormality in the industrial machine, and extracts data corresponding to the trigger from the state data when the trigger occurs. The processor stores the data corresponding to the trigger as the training data. | 2021-12-30 |
20210406706 | METHOD AND APPARATUS FOR PERFORMING ENTITY LINKING - Provided is a method for performing entity linking between a surface entity mention in a surface text and entities of a knowledge graph, including supplying the surface text to a contextual text representation model, pooling contextual representations of the tokens of a surface entity mention in the surface text with contextual representations of the other tokens within the surface text to provide a contextual entity representation vector representing the surface entity mention; supplying an identifier of a candidate knowledge graph entity to a knowledge graph embedding model, to provide an entity node embedding vector and combining the contextual entity representation vector with the entity node embedding vector to generate an input vector applied to a fully connected layer which provides an unnormalized output transformed by a softmax function into a normalized output processed to classify whether the surface entity mention corresponds to the candidate knowledge graph entity. | 2021-12-30 |
20210406707 | Feature and Case Importance and Confidence for Imputation in Computer-Based Reasoning Systems - Techniques are provided for imputation in computer-based reasoning systems. The techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining which cases have fields to impute (e.g., missing fields) in the computer-based reasoning model and determining conviction scores and/or imputation order information for the cases that have fields to impute. The techniques proceed by determining for which cases to impute data and, for each of the determined one or more cases with missing fields to impute data is imputed for the missing field, and the case is modified with the imputed data. Control of a system is then caused using the updated computer-based reasoning model. | 2021-12-30 |
20210406708 | MACHINE LEARNING BASED IDENTIFICATION AND CLASSIFICATION OF DATABASE COMMANDS - Aspects of the disclosure relate to a machine learning based identification and classification of database commands. A computing platform may retrieve, by a computing device and from a first database of a plurality of databases, a database command. Subsequently, the computing platform may identify, by the computing device and for the database command and based on a machine learning model, one or more database commands from the plurality of databases, wherein the one or more database commands perform operations similar to the database command. Then, the computing platform may determine, by the computing device and for the database command, a security score indicative of a level of vulnerability associated with the database command. Subsequently, the computing platform may provide, via an interactive graphical user interface, the database command and the security score. | 2021-12-30 |
20210406709 | AUTOMATIC BUILDING DETECTION AND CLASSIFICATION USING ELEVATOR/ESCALATOR/STAIRS MODELING-MOBILITY PREDICTION - A system, a method and a computer program product are provided to determine mobility pattern of one or more users for buildings, using machine learning model. The system may include at least one memory configured to store computer executable instructions and at least one processor configured to execute the computer executable instructions to obtain mobility features associated with the buildings in a geographic region, entry-exit data of the one or more users for the buildings in the geographic region The processor may be configured to determine, using trained machine learning model, one or more transport modes for the one or more buildings, based on the mobility features. The processor may be configured to determine, using a trained machine learning model, the mobility pattern of the one or more users based on the entry-exit data of the one or more users and the one or more transport modes for the buildings. | 2021-12-30 |
20210406710 | AUTOMATIC BUILDING DETECTION AND CLASSIFICATION USING ELEVATOR/ESCALATOR/STAIRS MODELING- USER PROFILING - A system, a method and a computer program product are provided to determine user profile of one or more users in one or more buildings, using a machine learning model. The system may include at least one memory configured to store computer executable instructions and at least one processor configured to execute the computer executable instructions to obtain mobility features associated with the one or more buildings in a geographic region. The processor may be configured to determine using a trained machine learning model, one or more transport modes for the one or more buildings. The processor may be further configured to obtain indoor mobility data of the one or more users based on the one or more transport modes for the one or more buildings. The processor may be further configured determine the user profile of the one or more users in the one or more buildings. | 2021-12-30 |
20210406711 | PREDICTING WEB APPLICATION PROGRAMMING INTERFACE CALL OPERATIONS USING MACHINE LEARNING TECHNIQUES - Methods, apparatus, and processor-readable storage media for predicting web API call operations using machine learning techniques are provided herein. An example computer-implemented method includes obtaining input data pertaining to one or more operations within one or more web application programming interface calls; predicting at least one operation to be requested in a given web application programming interface call by processing the input data and data pertaining to the given web application programming interface call using one or more machine learning techniques; and performing at least one automated action based at least in part on the at least one predicted operation. | 2021-12-30 |
20210406712 | Bias Source Identification and De-Biasing of a Dataset - A source of bias identification (SoBI) tool is provided that identifies sources of bias in a dataset. A bias detection operation is performed on results of a computer model, based on an input dataset, to generate groupings of values for a protected attribute corresponding to a detected bias in the operation of the computer model. The SoBI tool generates a plurality of sub-groups for each grouping of values. Each sub-group comprises an individual value, or a sub-range, for the protected attribute. The SoBI tool analyzes each of the sub-groups in the plurality of sub-groups, based on at least one source of bias identification criterion, to identify one or more sources of bias in the input dataset. The SoBI tool outputs a bias notification to an authorized computing device specifying the one or more sources of bias in the input dataset. | 2021-12-30 |
20210406713 | Intelligent Agent - A computing system aggregates information from a plurality of information channels associated with a computing device and a user of the computing device. A user configures the access for the computing system to specific information channels at a user interface. Based on a knowledge base and by machine learning techniques, the computing system analyzes the aggregated information to identify information relevant to an intelligent action for execution on behalf of the user. The computing system identifies the intelligent action in the context of the user's preferences and permissions granted to the computing system. The computing system initiates execution of the intelligent action based on a confidence level derived from analysis of information contained the knowledge base and historical decisioning information. The computing system receives feedback for an executed action and incorporates the feedback in the knowledge base for future decisioning based on aggregated information. | 2021-12-30 |
20210406714 | Internet-based method for dividing personal property items among two or more members of an estate by merging individual estate members? numerical and explicitly non-financial rankings of all items in the estate?s inventory. - An internet-based method for mathematically optimizing the distribution of an estate's idiosyncratically (non-financially) valued personal property items among two or more estate members. The method is predicated on the automated merger of independent and strictly numerical rankings of every item in an estate inventory as provided by every estate beneficiary. The method explicitly avoids financial valuation. The result is the distribution of all items in the estate inventory to all parties in the estate according to rules that optimize a correlation between an estate member's strictly numerically ranked interest in an estate item with the likelihood that said estate member will receive said estate item. | 2021-12-30 |
20210406715 | INTELLIGENT SELECTOR CONTROL FOR USER INTERFACES - Methods and systems for intelligently recommending selections for a selector control are disclosed. The method includes receiving a recommendation request from a selector control client, the recommendation request comprising a search string and a unique identifier of a user interacting with a selector control; identifying user identifiers of usernames matching the search string; retrieving machine learning features corresponding to the user identifiers of usernames matching the search string; applying a machine learning model to the retrieved machine learning features to assign weights to the retrieved machine learning features; computing recommendation scores for the user identifiers based on the assigned weights to the retrieved machine learning features; ranking the user identifiers based on the recommendation scores; and forwarding a ranked list of user identifiers to the selector control client for displaying in the selector control for selection by the user interacting with the selector control. | 2021-12-30 |
20210406716 | METHODS AND SYSTEMS FOR ACQUIRING AND MANIPULATING RELEVANT INFORMATION USING MACHINE LEARNING - Systems and methods may be used to generate and use a structured form representation and structured metadata. The structured form representation and structured metadata may include information relevant to a particular context and may be used to update document templates, import new documents and update document versions into software, automate data entry for document completion, update records to include new and or updated information, and provide other functionality of an information service. | 2021-12-30 |
20210406717 | ENABLING EFFICIENT MACHINE LEARNING MODEL INFERENCE USING ADAPTIVE SAMPLING FOR AUTONOMOUS DATABASE SERVICES - Herein are approaches for self-optimization of a database management system (DBMS) such as in real time. Adaptive just-in-time sampling techniques herein estimate database content statistics that a machine learning (ML) model may use to predict configuration settings that conserve computer resources such as execution time and storage space. In an embodiment, a computer repeatedly samples database content until a dynamic convergence criterion is satisfied. In each iteration of a series of sampling iterations, a subset of rows of a database table are sampled, and estimates of content statistics of the database table are adjusted based on the sampled subset of rows. Immediately or eventually after detecting dynamic convergence, a machine learning (ML) model predicts, based on the content statistic estimates, an optimal value for a configuration setting of the DBMS. | 2021-12-30 |
20210406718 | LEVERAGING DIALOGUE HISTORY IN UPDATED DIALOGUE - A method of leveraging a dialogue history of a conversational computing interface to execute an updated dialogue plan. The method comprises maintaining an annotated dialogue history of the conversational computing interface. The annotated dialogue history includes a plurality of traced steps defining a data-flow including input data used to execute a context-dependent operation and output data recorded from a previous execution of the context-dependent operation. The method further comprises recognizing an updated dialogue plan including a prefix of executable steps and an updated executable step following the prefix. The method further comprises automatically computer-recognizing that the prefix of executable steps of the updated dialogue plan matches a corresponding prefix of traced steps in the annotated dialogue history. The method further comprises re-using the data-flow from the prefix of traced steps in the annotated dialogue history to automatically determine input data of the updated executable step. | 2021-12-30 |
20210406719 | MODIFIED MEDIA DETECTION - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting modified media are disclosed. In one aspect, a method includes the actions of receiving an item of media content. The actions further include providing the item as an input to a model that is configured to determine whether the item likely includes audio of a user's voice that was not spoken by the user or likely includes video of the user that depicts actions of the user that were not performed by the user. The actions further include receiving, from the model, data indicating whether the item likely includes audio of the user's voice that was not spoken by the user or includes video of the user that depicts actions of the user that were not performed by the user. The actions further include determining whether the item likely includes deepfake content. | 2021-12-30 |
20210406720 | MULTIPLE GRANULARITY CLASSIFICATION - Systems, methods, and related technologies for classification are described. Network traffic from a network may be accessed and an entity may be selected. One or more values associated with one or more properties associated with the entity may be determined. The one or more values may be accessed from the network traffic. A first model associated with a first level of granularity is accessed. A first classification result of the entity based on the first model is determined by a processing device. A second model associated with a second level of granularity is accessed. The second level of granularity is higher than the first level of granularity and the second model is accessed based on the first classification result. A second classification result of the entity based on the second model is determined. At least one of the first classification result or the second classification result is stored. | 2021-12-30 |
20210406721 | SYSTEMS AND METHODS FOR PREDICTING PERFORMANCE - The present disclosure relates to system and methods for predicting performance caused by software code changes. For this purpose, an augmented machine learning model predicts a latency of software module with updated code executed in a production environment. In some aspects, the latency is predicted based on a change of deviation that is determined by comparing the latency of the software module with updated code and the latency of the software module without updated code, whereas the software modules are executed in environments different from the production environment. | 2021-12-30 |
20210406722 | SIMULATING DEGRADED SENSOR DATA - Aspects of the disclosure relate to generating simulated degraded sensor data. For instance, first sensor data collected by a sensor of a perception system of an autonomous vehicle may be received. The first sensor data may be inputted into simulated degraded sensor data for a particular degrading condition. The simulated degraded sensor data may be used to evaluate or train a model for detecting objects of the perception system. | 2021-12-30 |
20210406723 | INTERACTIVE SEARCH TRAINING - Aspects of the present disclosure relate to interactive search training. A training canvas comprises results associated with a search query. The training canvas may be used as part of a training session that occurs during normal use of a search platform. When the search platform is first used, the results may be provided based on an existing model. An irrelevant result may be removed from the training canvas, such that a replacement result is added in its place. Additionally, results may be reordered, thereby indicating a ranking with which results should be displayed. Such interactions with the training canvas may be used to generate training data, such that a new model is trained accordingly. Thus, interactions with the training canvas yield high-quality training data that is usable to generate a model having equal or greater performance than a model that was trained using an equivalent amount of implicit training data. | 2021-12-30 |
20210406724 | LATENT FEATURE DIMENSIONALITY BOUNDS FOR ROBUST MACHINE LEARNING ON HIGH DIMENSIONAL DATASETS - Computer-implemented methods and systems for quantifying appropriate machine learning model complexity corresponding to training dataset are provided. The method comprises monitoring, using one or more processors, N observed variables, v | 2021-12-30 |
20210406725 | CLASSIFYING ORGANIZATIONS BASED ON TRANSACTIONAL DATA ASSOCIATED WITH THE ORGANIZATIONS - A method for classifying organizations involves obtaining, for an unknown organization, transactional data representing a multitude of transactions. The transactional data comprises a descriptive text for each of the multitude of transactions. The method further involves processing the descriptive text for each of the multitude of transactions to obtain one vector representing the unknown organization, categorizing the unknown organization using a classifier applied to the vector, and identifying a software service for the unknown organization, according to the categorization. | 2021-12-30 |
20210406726 | METHOD AND SYSTEM FOR IMPLEMENTING SYSTEM MONITORING AND PERFORMANCE PREDICTION - Described is an improved approach to implement an offline learning approach for machine learning that employs a window-based technique for predicting values within the window, and where outliers are identified and discarded from consideration. This approach efficiently permits offline learning to be employed in a manner that minimizes false positives, while also improving the quality of the data should retaining be required. | 2021-12-30 |
20210406727 | MANAGING DEFECTS IN A MODEL TRAINING PIPELINE USING SYNTHETIC DATA SETS ASSOCIATED WITH DEFECT TYPES - The disclosure herein describes managing defects in a model training pipeline. A synthetic data set is generated that is associated with a defect type and a lifecycle stage of the model training pipeline, and baseline performance metrics associated with the defect type are generated. Based on a code change to the pipeline, a test model is trained using the pipeline and the synthetic data set, and test performance metrics are collected based on the test model and associated with the defect type. Based on comparing the baseline performance metrics and the test performance metrics, a defect of a particular defect type is identified in the pipeline. An indicator of the defect is provided that includes the defect type and the lifecycle stage with which the synthetic data set is associated, whereby a defect correction process is enabled to remedy the defect based on the associated defect type and the lifecycle stage. | 2021-12-30 |
20210406728 | Human Experience Insights Architecture - A system, method, and computer-readable medium are disclosed for performing a human experience enhancement operation. In various embodiments the human experience enhancement operation includes performing an extraction operation, the extraction operation extracting human experience related data from a human experience data source, the extraction operation receiving the human experience related data and abstracting the human experience related data into standardized human experience concepts; performing an enrichment operation, the enrichment engine receiving the standardized human experience concepts and categorizing the standardized human experience concepts into corresponding classes; performing an analysis operation, the analysis engine receiving the corresponding classes and mapping the corresponding classes to a continuum of innovation to provide a mapped human experience insight; and, performing a results operation, the results engine receiving the mapped human experience insight, the results operation using the mapped human experience insight generate a human experience recommendation. | 2021-12-30 |
20210406729 | Refining Mapped Human Experiences Insights within a Human Experience Flow - A system, method, and computer-readable medium are disclosed for performing a human experience operation. The human experience operation includes receiving standardized human experience concepts and categorized human experience concepts; performing an analysis operation, the analysis engine receiving the categorized corresponding concepts and mapping the categorized human experience concepts to human experience enhancement objectives to provide a mapped human experience insight; and, providing the mapped human experience insight to a results engine, the results engine using the mapped human experience insight generate a human experience recommendation. | 2021-12-30 |
20210406730 | METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR PROCESSING INFORMATION - Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing information. According to an example embodiment, the method includes: acquiring a service request record set, each service request record in the service request record set relating to a problem encountered by a user when the user is provided with a service and a solution to the problem; constructing a language model based on a first subset in the service request record set and an initial model, the initial model being trained using a predetermined corpus and configured to determine vector representations of words and sentences in the corpus; and constructing a classification model based on a second subset in the service request record set and the language model, the classification model being capable of determining a solution to a pending problem, and the first subset being different from the second subset. | 2021-12-30 |
20210406731 | NETWORK-IMPLEMENTED INTEGRATED MODELING SYSTEM FOR GENETIC RISK ESTIMATION - A network-implemented integrated modeling system for genetic risk estimation uses guideline factor information and other input information to determine models usable for estimating genetic risk of disease in patients. Models are determined using established network connections with external sources. Health history information is collected from a patient, which is then processed against a relevant model to estimate the genetic risk of a disease corresponding to that model in that patient. Based on results of the estimation, a pedigree chart representing personal and family risk for the patient is generated and output to the patient and/or health care representatives of the patient. The results may be disseminated to one or more distributed computing environments from a central server which performs the genetic risk estimation. In some cases, follow-up validations performed using further collected data are used to update the constraints used to determine the models. | 2021-12-30 |
20210406732 | METHOD FOR BUILDING MACHINE LEARNING MODELS FOR ARTIFICIAL INTELLIGENCE BASED INFORMATION TECHNOLOGY OPERATIONS - A method and system for forecasting resource utilization of an information technology system having a plurality of system components. The method includes classifying the plurality of system components based on at least one resource utilization metric. The method also includes determining at least one reference component in each class from among the components classified within the respective class. The method also includes building a representative machine learning model for each reference component in each class. The method also includes applying the representative machine learning model to all system components within the respective class. Applying the representative machine learning model to all system components within the respective class forecasts the resource utilization of all system components in the information technology system without building a machine learning model for each system component in the information technology system. | 2021-12-30 |
20210406733 | SYSTEM AND METHODS FOR PREDICTION COMMUNICATION PERFORMANCE IN NETWORKED SYSTEMS - A system for processing performance prediction decisions includes one or more processors configured to execute one or more program modules. The modules are configured to receive, via the one or more processors, a prediction for an account at a prediction timestamp. The modules are also configured to identify, via the one or more processors, a prediction rule using attributes from the prediction. Responsive to the prediction rule having a network trigger associated therewith, the modules are configured to retrieve, via the one or more processors, a network trigger time associated with the network trigger, compare, via the one or more processors, the prediction timestamp to the network trigger time, and apply, via the one or more processors, a prediction decision based on the comparison of the prediction timestamp and the network trigger time. Applying the prediction decision includes determining a confidence level that a communication associated with the prediction will occur by a given time. | 2021-12-30 |
20210406734 | Cognitive Machine Learning System for Mixed Mode Aviation - Embodiments include systems and methods for generating recommendations using an aviation cognitive digital agent. A trained Deep Neural Language Network (DNLN) configured to map users and actions to a shared semantic space can be received, where the DNLN is trained using mixed domain historic aviation data that includes user features and action features. Input data including features descriptive of an aviation event can be received. Based on the input data, an output vector can be generated using the trained DNLN that maps the input data to the shared semantic space. The output vector can be processed with a plurality of candidate vectors to generate one or more recommendations for the aviation event, wherein the candidate vectors correspond to candidate actions for the aviation event and the candidate vectors have been mapped to the shared semantic space. | 2021-12-30 |
20210406735 | SYSTEMS AND METHODS FOR QUESTION-AND-ANSWER SEARCHING USING A CACHE - Disclosed are methods, systems, devices, apparatus, media, design structures, and other implementations, including a method that includes receiving, at a local device from a remote device, query data representative of a question relating to source content of a source document, and determining whether one or more pre-determined questions stored in a question-answer cache maintained at the local device matches the query data according to one or more matching criteria. The method further includes obtaining from the question-answer cache, in response to a determination that at least one of the pre-determined questions matches the query data received from the remote device, at least one answer data item, associated with at least one pre-determined question, corresponding to an answer to the question relating to the source content. | 2021-12-30 |
20210406736 | SYSTEM AND METHOD OF CONTENT RECOMMENDATION - A system and method for generating recommended content are provided. The system comprises a processor and a memory, in communication with the processor, that when executed by the processor performs the method. The method comprises receiving at least two context tags associated with a user, identifying from a content repository related content that are related to each context tag through semantics or frequency of use, generating a similarity vector for each context tag that correlates the context tag with the related content for that context tag, inputting the similarity vectors to an inference network, determining a probability distribution for each of the related content based on the output of the inference network, and identifying the recommended content from the related content, based at least in part on a threshold and the probability distributions of the related content. The initial recommended content may be determined through a calibration procedure. The recommended content may be recommended in the form of an experience and may be updated over time. | 2021-12-30 |
20210406737 | SYSTEM AND METHODS FOR AGGREGATING FEATURES IN VIDEO FRAMES TO IMPROVE ACCURACY OF AI DETECTION ALGORITHMS - Methods and systems are provided for aggregating features in multiple video frames to enhance tissue abnormality detection algorithms, wherein a first detection algorithm identifies an abnormality and aggregates adjacent video frames to create a more complete image for analysis by an artificial intelligence detection algorithm, the aggregation occurring in real time as the medical procedure is being performed. | 2021-12-30 |
20210406738 | METHODS AND SYSTEMS FOR PROVIDING ACTIVITY FEEDBACK UTILIZING COGNITIVE ANALYSIS - Embodiments for providing activity feedback are provided. Information associated with a user performing an activity is received. A user biomechanical representation is generated based on the received information. A corpus associated with the activity is analyzed. An ideal biomechanical representation is generated based on the analyzing of the corpus associated with the activity. The user biomechanical representation is compared to the ideal biomechanical representation. Feedback for the user is generated based on the comparison of the user biomechanical model to the ideal biomechanical representation. | 2021-12-30 |
20210406739 | PREDICTIVE DATA ANALYSIS TECHNIQUES USING BIDIRECTIONAL ENCODINGS OF STRUCTURED DATA FIELDS - There is a need for more effective and efficient predictive data analysis based at least in part on structured data. This need can be addressed by, for example, solutions for performing predictive data analysis using bidirectional encoder deep learning models that are configured to process structured data attributes. In one example, a method includes identifying a group of training structured data fields; generating a group of per-field tokenized values for each training structured data field; generating a bidirectional encoder deep learning model based at least in part on each group of per-field tokenized values for a training structured data field; and performing one or more prediction-based actions based at least in part on the trained bidirectional encoder deep learning model. | 2021-12-30 |
20210406740 | METHOD AND SYSTEM FOR ESTIMATING RELOCATION COSTS - A method for improving the estimation of relocation costs including the steps of: generating a relocation costs data-model; performing a dual-model algorithm on the relocation costs data-model to determine a preliminary relocation costs predictive model for a relocation service; receiving a first dataset of a subject to be relocated; analyzing the first dataset with the preliminary relocation costs predictive model to generate a preliminary relocation costs for the relocation service; displaying, on a display of a remote device, the preliminary relocation costs; performing the dual-model algorithm on the relocation costs data-model to determine a supplemental relocation costs predictive model for the relocation service; receiving a second dataset of the subject to be relocated; analyzing the second dataset with the supplemental relocation costs predictive model to generate a supplemental relocation costs for the relocation service; and displaying, on the display of the remote device, the supplemental estimated relocation costs. | 2021-12-30 |
20210406741 | SELF-SUPERVISED SELF SUPERVISION BY COMBINING PROBABILISTIC LOGIC WITH DEEP LEARNING - The present disclosure relates to devices and methods for determining new virtual evidence to use with a deep probabilistic logic module. The devices and methods may receive output from a deep probabilistic logic module in response to running an initial set of virtual evidence through the deep probabilistic logic module. The devices and methods may use the output to automatically propose at least one factor as new virtual evidence for use with the deep probabilistic logic module. The devices and methods may add the new virtual evidence to the deep probabilistic logic module. | 2021-12-30 |
20210406742 | ABSOLUTE AND RELATIVE IMPORTANCE TREND DETECTION - In an embodiment, a method includes acquiring a current condition indicator of a condition indicator set associated with an operating condition of a machine, the condition indicator set indicating sensor readings associated with an operating element of the machine under the operating condition. The method also includes determining, by a data server, a relative trend significance over a trend window of the condition indicator set based, at least in part, on an evaluation of the trend window in relation to a historical window of the condition indicator set. The method also includes determining, by the data server, whether trend criteria associated with the operating element is satisfied, where the trend criteria may include criteria related to the relative trend significance. The method also includes executing, by the data server, an alerting process in response to the determining that the trend criteria is satisfied. | 2021-12-30 |
20210406743 | PERSONALIZED APPROACH TO MODELING USERS OF A SYSTEM AND/OR SERVICE - Dynamic state-space modeling within a special purpose hardware platform to determine non-conversion risks for each trial user and churn risks for each active subscriber having exhibited a sequence of behaviors. The state-space model may be operable to determine a loss risk for each of a provider's active trial users and/or subscribers. | 2021-12-30 |
20210406744 | EFFICIENTLY CONSTRUCTING REGRESSION MODELS FOR SELECTIVITY ESTIMATION - A model generator constructs a model for estimating selectivity of database operations by determining a number of training examples necessary for the model to achieve a target accuracy and by generating approximate selectivity labels for the training examples. The model generator may train the model on an initial number of training examples using cross-validation. The model generator may determine whether the model satisfies the target accuracy and iteratively and geometrically increase the number of training examples based on an optimized geometric step size (which may minimize model construction time) until the model achieves the target accuracy based on a defined confidence level. The model generator may generate labels using a subset of tuples from an intermediate query expression. The model generator may iteratively increase a size of the subset of tuples used until a relative error of the generated labels is below a target threshold. | 2021-12-30 |
20210406745 | FORECASTING FIELD LEVEL CROP YIELD DURING A GROWING SEASON - A method for predicting field specific crop yield recommendations for a field may be accomplished using a server computer system that is configured and programmed to receive over a digital communication network, electronic digital data representing agricultural data records, including remotely sensed spectral property of plant records and soil moisture records. Using digitally programmed data record aggregation instructions, the computer system is programmed to receive digital data representing including remotely sensed spectral property of plant records and soil moisture records. Using the digitally programmed data record aggregation instructions, the computer system is programmed to aggregate the one or more digital agricultural records to create and store, in computer memory, one or more geo-specific time series over a specified time. Using the digitally programmed data record aggregation instructions, the computer system is programmed to select one or more representative features from the one or more geo-specific time series and create, for each specific geographic area, a covariate matrix in computer memory comprising the representative features selected from the one or more geo-specific time series. Using mixture linear regression instructions, the computer system is programmed to assign a probability value to a component group in a set of parameter component groups, where each component group within the set of parameter component groups includes one or more regression coefficients calculated from a probability distribution and an error term calculated from a probability distribution. Using distribution generation instructions, the computer system is programmed to generate the probability distributions used to determine the one or more regression coefficients and the error term, the probability distribution used to generate the error term is defined with a mean parameter set at zero and a variance parameter set to a field specific bias coefficient. | 2021-12-30 |
20210406746 | TUNABLE QUANTUM COUPLER FACILITATING A QUANTUM GATE BETWEEN QUBITS - Devices and/or computer-implemented methods to facilitate a quantum gate between qubits using a tunable coupler and a capacitor device are provided. According to an embodiment, a quantum coupler device can comprise a tunable coupler coupled between terminals of a same polarity of a first qubit and a second qubit, the tunable coupler configured to control a first coupling between the first qubit and the second qubit. The quantum coupler device can further comprise a capacitor device coupled to terminals of an opposite polarity of the first qubit and the second qubit, the capacitor device configured to provide a second coupling that is opposite in sign relative to the first coupling. | 2021-12-30 |
20210406747 | PERFORMING QUANTUM FILE COPYING - Performing quantum file copying is disclosed herein. In one example, upon receiving a request to copy a source quantum file comprising a plurality of source qubits, a quantum file manager accesses a quantum file registry record identifying the plurality of source qubits and a location of each of the plurality of source qubits. The quantum file manager next allocates a plurality of target qubits equal in number to the plurality of source qubits, and copies data stored by each of the source qubits into a corresponding target qubit. The quantum file manager then generates a target quantum file registry record that identifies the plurality of target qubits and their locations. In some examples, a quantum file move operation may be performed by deleting the source quantum file after the copy operation, and updating the target quantum file registry record with the same quantum file identifier as the source quantum file. | 2021-12-30 |
20210406748 | PERFORMING QUANTUM FILE CONCATENATION - Performing quantum file concatenation is disclosed herein. In one example, a quantum file manager receives a request to concatenate a first quantum file comprising a first plurality of qubits and a second quantum file comprising a second plurality of qubits. Responsive to receiving the request, the quantum file manager concatenates the first quantum file and the second quantum file into a concatenated quantum file comprising a third plurality of qubits, wherein the third plurality of qubits comprises a same number of qubits as a union of the first plurality of qubits and the second plurality of qubits, and stores an identical sequence of data values as the first plurality of qubits followed by the second plurality of qubits. | 2021-12-30 |
20210406749 | QUANTUM MEASUREMENT EMULATION ERROR MITIGATION PROTOCOL FOR QUANTUM COMPUTING - Systems and methods for performing open-loop quantum error mitigation using quantum measurement emulations are provided. The open-loop quantum error mitigation methods do not require the performance of state readouts or state tomography, reducing hardware requirements and increasing overall computation speed. To perform a quantum measurement emulation, an error mitigation apparatus is configured to stochastically apply a quantum gate to a qubit or set of qubits during a quantum computational process. The stochastic application of the quantum gate projects the quantum state of the affected qubits onto an axis, reducing a trace distance between the quantum state and a desired quantum state. | 2021-12-30 |
20210406750 | METHOD AND ARRANGEMENT FOR RESETTING QUBITS - A method, system, and arrangement for resetting qubits are disclosed. An example system includes one or more quantum circuit refrigerators for resetting qubits. Each of the quantum circuit refrigerators includes a tunneling junction and a control input for receiving a control signal. Photon-assisted single-electron tunneling takes place across the respective tunneling junction in response to a control signal. Capacitive or inductive coupling elements between the qubits and the quantum circuit refrigerators couple each qubit to the quantum circuit refrigerator(s). The qubits, quantum circuit refrigerators, and coupling elements are located in a cryogenically cooled environment. A common control signal line to the control inputs crosses into the cryogenically cooled environment from a room temperature environment. | 2021-12-30 |
20210406751 | QUANTUM CIRCUIT WITH TAILORED RYDBERG STATES - In the context of gate-model quantum computing, atoms (or polyatomic molecules) are excited to respective Rydberg states to foster intra-gate interactions. Rydberg states with relatively high principal quantum numbers are used for relatively distant intra-gate interactions and require relatively great inter-gate separations to avoid error-inducing inter-gate interactions. Rydberg states with relatively low principal quantum numbers can be used for intra-gate interactions over relatively short intra-gate distances and require relatively small inter-gate separations to avoid error-inducing inter-gate interactions. The relatively small inter-gate separations provide opportunities for parallel gate executions, which, in turn, can provide for faster execution of the quantum circuit constituted by the gates. By using Rydberg states with relatively high principal quantum numbers where required, and Rydberg states with relatively low principal quantum numbers where possible, an optimal tradeoff between intra-gate interaction flexibility and inter-gate parallelism can be achieved. | 2021-12-30 |
20210406752 | Distributed Quantum Computing System - In a general aspect, user requests for access distributed quantum computing resources in a distributed quantum computing system are managed. In a general aspect, a job request for accessing a quantum computing resource is received. The job request includes a user id and a program. On authentication of a user associated with the job request, a job identifier is assigned to the job request, and a particular quantum computing resource is selected for the job request. The job request is individualized based on user permissions and pushed onto a queue to be processed for execution by the quantum computing resource. | 2021-12-30 |
20210406753 | System and Method for Optimizing Quantum Circuit Synthesis - A method is provided for synthesizing quantum circuits while reducing the T-count, comprising, for a plurality of qubits: determining a target unitary and executing a set of candidate operations W with a single T gate and computing a specific function f of U W | 2021-12-30 |
20210406754 | Quantum Computer, Non-Transitory Computer Readable Media Storing Program, Quantum Calculation Method, And Quantum Circuit - A quantum computer includes: a setting unit configured to set a parameter group of n layers based on each coefficient in a linear sum of unitary operators whose number is 2 to the n-th power, wherein the parameter group of k-th (2≤k≤n) layer is recursively set based on the parameter group of (k−1)-th layer; a quantum gate having n+m qubits including n auxiliary qubits and m target qubits, and configured to execute a predetermined calculation on an input value input to each qubit based the parameter group of n layers; and a specification unit configured to specify the linear sum of the unitary operators based on a calculation result of the quantum gate. | 2021-12-30 |
20210406755 | COMPILING METHOD AND SYSTEM WITH PARTIAL SYNTHETIZATION OF QUANTUM COMPUTER COMPLIANT QUANTUM CIRCUITS - The present disclosure relates to a compiling method ( | 2021-12-30 |
20210406756 | COOLING HIGH MOTIONAL STATES IN ION TRAP QUANTUM COMPUTERS - Aspects of the present disclosure describe techniques for cooling motional states in an ion trap for quantum computers. In an aspect, a method includes performing Doppler cooling and sideband cooling to sweep motional states associated with a motional mode to a zero motional state; applying a gate interaction on a red sideband; detecting, a population of non-zero motional states of the motional mode that remains after performing the Doppler cooling and the sideband cooling; and removing at least part of the population. In another aspect, a method includes performing a Doppler cooling; applying a gate interaction on a red sideband; detecting whether a population of non-zero motional states of the motional mode remains after performing the Doppler cooling; and redistributing the population of the non-zero motional states by Doppler cooling when a population is detected. A quantum information processing (QIP) system that performs these methods is also described. | 2021-12-30 |
20210406757 | ACTIVE STABILIZATION OF COHERENT CONTROLLERS USING NEARBY QUBITS - Aspects of the present disclosure describe techniques that involve an active stabilization of coherent controllers using nearby qubits. In an aspect, a quantum information processing (QIP) system for stabilizing phase damping in qubits is described that provides a first and a second qubit ion, measuring magnetic field fluctuations using the second qubit ion, and generates one or more magnetic fields based on the measured magnetic field fluctuations, the one or more magnetic fields being applied near the first qubit ion to cancel the magnetic field fluctuations to stabilize the phase damping of the first qubit ion. Another such QIP system performs provides a first and a second qubit ion, locks a local oscillator to a frequency reference associated with the second qubit ion, and tracks, using the local oscillator, a frequency of the first qubit ion based on the frequency reference. Methods associated with these QIP systems are also described. | 2021-12-30 |
20210406758 | DOUBLE-BARRELED QUESTION PREDICTOR AND CORRECTION - A computer-implemented method includes gathering data samples into a data set, correcting for imbalance in the data set to produce a corrected data set by applying active learning to the data set to increase a number of double barreled question data samples occurring in the data set, selecting an optimal machine learning model for the corrected data set, training the optimal machine learning model using the corrected data set, operating the optimal machine learning model on new data to produce a prediction result, and generating a visual representation of at least one prediction results. | 2021-12-30 |
20210406759 | SYSTEM FOR DYNAMIC ALLOCATION OF NAVIGATION TOOLS BASED ON LEARNED USER INTERACTION - Systems, computer program products, and methods are described herein for dynamic allocation of navigation tools based on learned user interaction. The present invention is configured to generate a training dataset based on at least the information associated with the interaction of the user with the one or more GUI grids, information associated with the one or more interactions of the one or more peers with the one or more GUI grids, information associated with the user, and information associated with the one or more peers; initiate one or more machine learning algorithms on the training dataset; receive, via the user computing device, a user selection of an unseen navigation tool for placement on the GUI; and classify the unseen navigation tool using the first set of parameters to predict a placement of the unseen navigation tool in at least one of one or more GUI grids associated with the GUI. | 2021-12-30 |
20210406760 | MODEL TRANSFER LEARNING ACROSS EVOLVING PROCESSES - Systems, computer-implemented methods, and computer program products to facilitate model transfer learning across evolving processes 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 condition definition component that defines one or more conditions associated with use of a model trained on first traces of a first process to make a prediction on one or more second traces of a second process. The computer executable components can further comprise a guardrail component that determines whether to use the model to make the prediction. | 2021-12-30 |
20210406761 | DIFFERENTIABLE USER-ITEM CO-CLUSTERING - The present concepts relate to a differentiable user-item co-clustering (“DUICC”) model for recommendation and co-clustering. Users' interaction with items (e.g., content) may be centered around information co-clusters—groups of items and users that exhibit common consumption behavior. The DUICC model may learn fine-grained co-cluster structures of items and users based on their interaction data. The DUICC model can then leverage the learned latent co-cluster structures to calculate preference stores of the items for a user. The top scoring items may be presented to the user as recommendations. | 2021-12-30 |
20210406762 | METHODS FOR REFINING DATA SET TO REPRESENT OUTPUT OF AN ARTIFICIAL INTELLIGENCE MODEL - A computer-implemented method for refining dataset to accurately represent output of an artificial intelligence model includes generating a plurality of data points used to interpret a decision of an artificial intelligence model. A subset of data points from the generated plurality of data points satisfying one or more constraints is identified. A linear model is applied on the identified subset of data points satisfying the one or more constraints. One or more insights illustrating the decision of the artificial intelligence model is generated. | 2021-12-30 |
20210406763 | METHOD AND SYSTEM FOR OPTIMIZING LEARNING MODELS POST-DEPLOYMENT - A method and system for optimizing a learning model post-deployment. Specifically, the disclosed method and system re-optimize—i.e., re-train and/or re-validate—machine learning and/or artificial intelligence algorithms that have already been deployed into a production environment. During post-deployment, the re-optimization process may transpire following the advent of varying model re-adjustment triggers. | 2021-12-30 |
20210406764 | METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR MONITORING FIELD DEVICE - The present disclosure relates to a method, an electronic device, and a computer program product for monitoring a field device. For example, a method for monitoring a field device is provided. The method may include receiving facility information data associated with locations of a group of field devices and a sensing data set acquired by a sensing apparatus arranged near the group of field devices. The method may further include determining, according to a determination that sensing data associated with at least one field device in the group of field devices in the sensing data set is abnormal, a target location of the at least one field device based on the facility information data. In addition, the method may further include generating navigation information from a source location where a user is located to the target location. | 2021-12-30 |
20210406765 | PARTIALLY-OBSERVED SEQUENTIAL VARIATIONAL AUTO ENCODER - A computer-implemented method of training a model comprising a sequence of stages, each stage in the sequence comprises: a VAE comprising a respective first encoder arranged to encode a respective subset of the real-world features into a respective latent space representation, and a respective first decoder arranged to decode from the respective latent space representation to a respective decoded version of the respective set of real-world features; at least each but the last stage in the sequence comprises: a respective second decoder arranged to decode from the respective latent space representation to predict one or more respective actions; and each successive stage in the sequence following the first stage, each succeeding a respective preceding stage in the sequence, further comprises: a sequential network arranged to transform from the latent representation from the preceding stage to the latent space representation of the successive stage. | 2021-12-30 |
20210406766 | AUGMENTED GAMMA BELIEF NETWORK OPERATION - A method, system and computer readable medium for generating a cognitive insight comprising: receiving data, the data comprising a plurality of examples, each of the plurality of examples comprising an input object and a desired output value, at least some of the plurality of examples being based upon feedback from a user; performing a machine learning operation on the data, the machine learning operation comprising performing an augmented gamma belief network operation, the augmented gamma belief network operation producing an inferred function based upon the data; and, generating a cognitive insight based upon the cognitive profile generated using the inferred function generated by the augmented gamma belief network operation. | 2021-12-30 |
20210406767 | Distributed Training Method and System, Device and Storage Medium - The present application discloses a distributed training method and system, a device and a storage medium, and relates to technical fields of deep learning and cloud computing. The method includes: sending, by a task information server, a first training request and information of an available first computing server to at least a first data server; sending, by the first data server, a first batch of training data to the first computing server, according to the first training request; performing, by the first computing server, model training according to the first batch of training data, sending model parameters to the first data server so as to be stored after the training is completed, and sending identification information of the first batch of training data to the task information server so as to be recorded; wherein the model parameters are not stored at any one of the computing servers. | 2021-12-30 |
20210406768 | MULTITEMPORAL DATA ANALYSIS - A system for multitemporal data analysis is provided, comprising a directed computation graph service module configured to receive input data from a plurality of sources, analyze the input data to determine a best course of action for analyzing the input data, and split the input data for queueing to a general transformer service module or a decomposable service module based at least in part by analysis of the input data; a general transformer service module configured to receive data from the directed computation graph service module, and perform analysis on the received data; and a general transformer service module configured to receive data from directed computational graph module, and perform analysis on the received data. | 2021-12-30 |
20210406769 | INFORMATION PROCESSING SYSTEM AND COMPRESSION CONTROL METHOD - A dynamic driving plan generator generates a driving plan representing a dynamic partial driving target of a compressor and a decompressor based on input data input to the compressor. The compressor is partially driven according to the driving plan to generate compressed data of the input data. The decompressor is partially driven according to the driving plan to generate reconstructed data of the compressed data. The dynamic driving plan generator has already been learned based on evaluation values obtained for the driving plan. Each of the evaluation values corresponds to a respective one of evaluation indexes for the driving plan, and the evaluation values are values obtained when at least the compression of the compression and the reconstruction according to the driving plan is executed. The evaluation indexes include the execution time for one or both of the compression and the reconstruction of the data. | 2021-12-30 |
20210406770 | Method For Adjusting Machine Learning Models And System For Adjusting Machine Learning Models - A method for adjusting machine learning models in a system including a plurality of devices is suggested. The method includes providing a system including a plurality of devices, wherein the devices have computational resource capacities; providing one or more machine learning tasks; providing a repository of ML models for the one or more tasks, wherein a plurality of the ML models of a single task solve the same task with different computational resources requirements and different quality metrics; selecting a device of the plurality of devices of the system to execute a task, wherein the selected device has available computational resource capacities; and selecting, from the repository of ML models of the task to be executed, one of the ML models, wherein the computational resources requirements of the selected ML model do not exceed the available computational resource capacities of the selected device. Systems configured to perform the methods as disclosed herein are also suggested. | 2021-12-30 |
20210406771 | SYSTEMS AND METHODS FOR USING ARTIFICIAL INTELLIGENCE TO EVALUATE LEAD DEVELOPMENT - Systems and methods for providing a computer system for evaluating a candidate subject are provided. A program with instructions to receive a first communication amongst various communications is provided. Each communication has text data and the received communication is associated with a candidate subject. The program has instructions to extract a plurality of information from the text data of the received communication. A tag is assigned to each of the information in a subset of information. A subset of tags is applied in which an evaluation of the candidate subject is obtained. | 2021-12-30 |
20210406772 | RULES-BASED TEMPLATE EXTRACTION - A user may markup the training documents to identify salient terms in a set of training unstructured documents. The system may automatically generate an extraction ruleset for each salient term that can be manually modified or edited by the user. The user may also provide analysis rulesets for each of the salient terms using, for example, a no-code graphical user interface. A machine learning model can be trained to automatically extract and analyze the salient terms based on feature vectors built from the extraction rulesets and/or analysis rulesets of the salient terms. After training, the system may import a set of unstructured documents for term extraction and analysis by the trained machine learning model. The system may generate a report, such as a PDF or an interactive graphical user interface, summarizing the results of the extracted and analyzed salient terms. | 2021-12-30 |
20210406773 | TRANSFORMING METHOD, TRAINING DEVICE, AND INFERENCE DEVICE - With respect to a transforming method for execution by at least one computer, the transforming method includes transforming a first probability distribution on a space defined with respect to a hyperbolic space to a second probability distribution on the hyperbolic space. | 2021-12-30 |