43rd week of 2021 patent applcation highlights part 49 |
Patent application number | Title | Published |
20210334628 | REINFORCEMENT LEARNING TECHNIQUES FOR SELECTING A SOFTWARE POLICY NETWORK AND AUTONOMOUSLY CONTROLLING A CORRESPONDING SOFTWARE CLIENT BASED ON SELECTED POLICY NETWORK - Techniques are disclosed that enable automating user interface input by generating a sequence of actions to perform a task utilizing a multi-agent reinforcement learning framework. Various implementations process an intent associated with received user interface input using a holistic reinforcement policy network to select a software reinforcement learning policy network. The sequence of actions can be generated by processing the intent, as well as a sequence of software client state data, using the selected software reinforcement learning policy network. The sequence of actions are utilized to control the software client corresponding to the selected software reinforcement learning policy network. | 2021-10-28 |
20210334629 | HYBRID NEURAL NETWORK ARCHITECTURE WITHIN CASCADING PIPELINES - A multi-stage multimedia inferencing pipeline may be set up and executed using configuration data including information used to set up each stage by deploying the specified or desired models and/or other pipeline components into a repository (e.g., a shared folder in a repository). The configuration data may also include information a central inference server library uses to manage and set parameters for these components with respect to a variety of inference frameworks that may be incorporated into the pipeline. The configuration data can define a pipeline that encompasses stages for video decoding, video transform, cascade inferencing on different frameworks, metadata filtering and exchange between models and display. The entire pipeline can be efficiently hardware-accelerated using parallel processing circuits (e.g., one or more GPUs, CPUs, DPUs, or TPUs). Embodiments of the present disclosure can integrate an entire video/audio analytics pipeline into an embedded platform in real time. | 2021-10-28 |
20210334630 | MODEL PREDICTIVE CONTROL TECHNIQUES FOR AUTONOMOUS SYSTEMS - Apparatuses, systems, and techniques to infer a sequence of actions to perform using one or more neural networks trained, at least in part, by optimizing a probability distribution function using a cost function, wherein the probability distribution represents different sequences of actions that can be performed. In at least one embodiment, a model predictive control problem is formulated as a Bayesian inference task to infer a set of solutions. | 2021-10-28 |
20210334631 | ACTIVATION FUNCTION PROCESSING METHOD, ACTIVATION FUNCTION PROCESSING CIRCUIT, AND NEURAL NETWORK SYSTEM INCLUDING THE SAME - An activation function processing method includes processing a first activation function in a first mode by referring to a shared lookup table that includes a plurality of function values of the first activation function; and processing a second activation function in a second mode by referring to the shared lookup table, the second activation function being a different function than the first activation function. | 2021-10-28 |
20210334632 | DECISION TREE INTERFACE FOR NEURAL NETWORKS - A first set of features associated with a neural network are parameterized. A decision tree is generated from the first set of features. One or more adjustments for the neural network are received at the decision tree. A second set of features associated with the adjustments at the decision tree are parameterized. The parameterized first and second set of features are combined into a plurality of parameters. From the plurality, an adjusted neural network is generated. | 2021-10-28 |
20210334633 | NEUROMORPHIC COMPUTING DEVICE AND OPERATING METHOD THEREOF - A neuromorphic computing device includes a first memory cell array comprising a plurality of resistive memory cells and configured to output a plurality of read currents through a plurality of bit lines or source lines; a second memory cell array comprising a plurality of reference resistive memory cells and configured to output at least one reference current through at least one reference bit line or at least one reference source line; a current-to-voltage converting circuit configured to output a plurality of signal voltages respectively corresponding to the plurality of read currents and output at least one reference voltage corresponding to the at least one reference current; and an analog-to-digital converting circuit configured to convert the plurality of signal voltages to a plurality of digital signals using the at least one reference voltage and output the plurality of digital signals. | 2021-10-28 |
20210334634 | METHOD AND APPARATUS FOR IMPLEMENTING AN ARTIFICIAL NEURON NETWORK IN AN INTEGRATED CIRCUIT - An embodiment method for implementing an artificial neural network in an integrated circuit comprises obtaining an initial digital file representative of a neural network configured according to at least one data representation format, then detecting at least one format for representing at least part of the data of the neural network, then converting at least one detected representation format into a predefined representation format so as to obtain a modified digital file representative of the neural network, and then integrating the modified digital file into an integrated circuit memory. | 2021-10-28 |
20210334635 | NEURAL NETWORK ACCELERATOR CONFIGURED TO PERFORM OPERATION ON LOGARITHM DOMAIN - Disclosed is a neural network accelerator including a maximum value determiner outputting a maximum value based on a first magnitude component corresponding to first input data and a second magnitude component corresponding to second input data, a sign determiner outputting a sign component corresponding to the maximum value among a first sign component corresponding to the first input data and a second sign component corresponding to the second input data, as an output sign component, an offset operator quantizing a difference between the first magnitude component and the second magnitude component and outputting an output offset based on the first sign component, the second sign component, and the quantization result, and a magnitude operator calculating an output magnitude component of an output data based on the maximum value and the output offset. Each of the first input data and the second input data is data on a logarithm domain. | 2021-10-28 |
20210334636 | SYSTOLIC-CNN: AN OPENCL-DEFINED SCALABLE RUNTIME-FLEXIBLE PROGRAMMABLE ACCELERATOR ARCHITECTURE FOR ACCELERATING CONVOLUTIONAL NEURAL NETWORK INFERENCE IN CLOUD/EDGE COMPUTING - An OpenCL-defined scalable runtime-flexible programmable accelerator architecture for accelerating convolutional neural network (CNN) inference in cloud/edge computing is provided, referred to herein as Systolic-CNN. Existing OpenCL-defined programmable accelerators (e.g., field-programmable gate array (FPGA)-based accelerators) for CNN inference are insufficient due to limited flexibility for supporting multiple CNN models at runtime and poor scalability resulting in underutilized accelerator resources and limited computational parallelism. Systolic-CNN adopts a highly pipelined and paralleled one-dimensional (1-D) systolic array architecture, which efficiently explores both spatial and temporal parallelism for accelerating CNN inference on programmable accelerators (e.g., FPGAs). Systolic-CNN is highly scalable and parameterized, and can be easily adapted by users to efficiently utilize the coarse-grained computation resources for a given programmable accelerator. In addition, Systolic-CNN is runtime-flexible and can be time-shared to accelerate a variety of CNN models at runtime without the need to recompile the programmable accelerator kernel hardware or reprogram the programmable accelerator. | 2021-10-28 |
20210334637 | DYNAMIC PRECISION FOR NEURAL NETWORK COMPUTE OPERATIONS - In an example, an apparatus comprises a compute engine comprising a high precision component and a low precision component; and logic, at least partially including hardware logic, to receive instructions in the compute engine; select at least one of the high precision component or the low precision component to execute the instructions; and apply a gate to at least one of the high precision component or the low precision component to execute the instructions. Other embodiments are also disclosed and claimed. | 2021-10-28 |
20210334638 | MEMRISTIVE NANOFIBER NEURAL NETWORKS - Disclosed are various embodiments of memristive devices comprising a number of nodes. Memristive fibers are used to form conductive and memristive paths in the devices. Each memristive fiber may couple one or more nodes to one or more other nodes. In one case, a memristive device includes a first node, a second node, and a memristive fiber. The memristive fiber includes a conductive core and a memristive shell surrounding at least a portion of the conductive core along at least a portion of the memristive fiber. The memristive fiber couples the first node to the second node through a portion of the memristive shell and at least a portion of the conductive core | 2021-10-28 |
20210334639 | PROGRAMMABLE OUTPUT BLOCKS FOR ANALOG NEURAL MEMORY IN A DEEP LEARNING ARTIFICIAL NEURAL NETWORK - Numerous embodiments are disclosed for programmable output blocks for use with a VMM array within an artificial neural network. In one embodiment, the gain of an output block can be configured by a configuration signal. In another embodiment, the resolution of an ADC in the output block can be configured by a configuration signal. | 2021-10-28 |
20210334640 | ARTIFICIAL INTELLIGENCE SERVER AND METHOD FOR PROVIDING INFORMATION TO USER - In an artificial intelligence server for providing information to a user, the artificial intelligence server includes a communication unit configured to communicate with a plurality of artificial intelligence apparatuses deployed in a service area and a processor configured to receive at least one of speech data of the user or terminal usage information of the user from at least one of the plurality of artificial intelligence apparatuses, generate intention information of the user based on at least one of the received speech data or the received terminal usage information, generate status information of the user using the plurality of artificial intelligence apparatuses, determine an information providing device among the plurality of artificial intelligence apparatuses based on the generated status information of the user, generate output information to be outputted from the determined information providing device, and transmit a control signal for outputting the generated output information to the determined information providing device. | 2021-10-28 |
20210334641 | ARTIFICIAL INTELLIGENCE LAUNDRY TREATMENT APPARATUS - An artificial intelligent laundry treatment apparatus according to an embodiment of the present invention includes: a door including an external cover and an internal glass and configured to open and close a laundry entrance; a gasket formed on an inner circumferential surface of the laundry entrance; a door imaging sensor disposed to face the internal glass and configured to acquire a door image; a gasket imaging sensor configured to acquire a gasket image including a region of the gasket; and a processor configured to classify a state of the door on the basis of the door image, to acquire a gasket contamination degree on the basis of the gasket image, and to determine whether inside cleansing is required for a region including an inside of a drum on the basis of at least one of the classification result of the state of the door or the acquired gasket contamination degree. | 2021-10-28 |
20210334642 | APPARATUS AND METHOD FOR IMAGE PROCESSING, AND SYSTEM FOR TRAINING NEURAL NETWORK - The present disclosure generally relates to the field of deep learning technologies. An apparatus for generating a plurality of correlation images may include a feature extracting unit configured to receive a training image and extracting at least one or more of feature from the training image to generate a first feature image based on the training image; a normalizer configured to normalize the first feature image and generate a second feature image; and a shift correlating unit configured to perform a plurality of translational shifts on the second feature image to generate a plurality of shifted images, correlate each of the plurality of shifted images with the second feature image to generate the plurality of correlation images. | 2021-10-28 |
20210334643 | PROCESSING UNIT FOR PERFORMING OPERATIONS OF A NEURAL NETWORK - A processing unit is described that receives an instruction to perform a first operation on a first layer of a neural network, block dependency data, and an instruction to perform a second operation on a second layer of the neural network. The processing unit performs the first operation, which includes dividing the first layer into a plurality of input blocks, and operating on the input blocks to generate a plurality of output blocks. The processing unit then performs the second operation after the first operation has generated a set number of output blocks defined by the block dependency data. | 2021-10-28 |
20210334644 | NEURAL NETWORK TRAINING TECHNIQUE - Apparatuses, systems, and techniques to train one or more neural networks. In at least one embodiment, one or more neural networks are trained based, at least in part, on inferencing output from one or more second neural networks. | 2021-10-28 |
20210334645 | NOTIFICATIONS DETERMINED USING ONE OR MORE NEURAL NETWORKS - Apparatuses, systems, and techniques are presented to determine actions to be taken for data anomalies. In at least one embodiment, audio and video data captured for an environment of a user can be analyzed to detect one or more data anomalies and determine whether to notify this user depending on whether the anomalies are applicable to this user. | 2021-10-28 |
20210334646 | ROBUSTNESS-AWARE QUANTIZATION FOR NEURAL NETWORKS AGAINST WEIGHT PERTURBATIONS - A method of utilizing a computing device to optimize weights within a neural network to avoid adversarial attacks includes receiving, by a computing device, a neural network for optimization. The method further includes determining, by the computing device, on a region by region basis one or more robustness bounds for weights within the neural network. The robustness bounds indicating values beyond which the neural network generates an erroneous output upon performing an adversarial attack on the neural network. The computing device further averages all robustness bounds on the region by region basis. The computing device additionally optimizes weights for adversarial proofing the neural network based at least in part on the averaged robustness bounds. | 2021-10-28 |
20210334647 | METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DETERMINING OUTPUT OF NEURAL NETWORK - Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for determining an output of a neural network. A method for determining an output of a neural network includes acquiring a feature vector outputted by at least one hidden layer of the neural network and a plurality of weight vectors associated with a plurality of candidate outputs of the neural network, corresponding probabilities of the plurality of candidate outputs being determined based on the plurality of weight vectors and the feature vector; converting the plurality of weight vectors into a plurality of binary sequences respectively, and converting the feature vector into a target binary sequence; determining a binary sequence most similar to the target binary sequence from the plurality of binary sequences; and determining the output of the neural network from the plurality of candidate outputs based on the binary sequence. | 2021-10-28 |
20210334648 | Statically Generated Compiled Representations for Processing Data in Neural Networks - An electronic device includes a memory that stores input matrices A and B, a cache memory, and a processor. The processor generates a compiled representation that includes values for acquiring data from input matrix A when processing instances of input data through the neural network, the values including a base address in input matrix A for each thread from among a number of threads and relative offsets, the relative offsets being distances between elements of input matrix A to be processed by the threads. The processor then stores, in the local cache memory, the compiled representation including the base address for each thread and the relative offsets. | 2021-10-28 |
20210334649 | APPARATUS AND METHOD FOR DISTRIBUTED REINFORCEMENT LEARNING - An apparatus for distributed reinforcement learning includes: a local neural network for receiving state information regarding a surrounding environment and estimating an action execution probability from the state information according to a previously learned pattern estimation method; a loss estimation unit for applying learning to the local neural network by estimating a loss value from the action execution probability and a global action execution probability transmitted from a central server; a local experience memory for mapping and storing the state information and the action execution probability; a clustering unit for clustering the state information stored in the local experience memory according to a pre-designated method to classify the state information into state clusters having proxy state information configured beforehand; and a local proxy memory for mapping and storing the proxy state information and proxy action execution probability corresponding to each state cluster for transmitting to the central server. | 2021-10-28 |
20210334650 | ARTIFICIAL-INTELLIGENCE-BASED WATERWAY INFORMATION SYSTEM - Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level. | 2021-10-28 |
20210334651 | LEARNING POINT CLOUD AUGMENTATION POLICIES - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model to perform a machine learning task by processing input data to the model. For example, the input data can include image, video, or point cloud data, and the task can be a perception task such as classification or detection task. In one aspect, the method includes receiving training data including a plurality of training inputs; receiving a plurality of data augmentation policy parameters that define different transformation operations for transforming training inputs before the training inputs are used to train the machine learning model; maintaining a plurality of candidate machine learning models; for each of the plurality of candidate machine learning models: repeatedly determining an augmented batch of training data; training the candidate machine learning model using the augmented batch of the training data; and updating the maintained data. | 2021-10-28 |
20210334652 | METHOD AND DEVICE FOR ON-VEHICLE ACTIVE LEARNING TO BE USED FOR TRAINING PERCEPTION NETWORK OF AUTONOMOUS VEHICLE - A method of on-vehicle active learning for training a perception network of an autonomous vehicle is provided. The method includes steps of: an on-vehicle active learning device, (a) if a driving video and sensing information are acquired from a camera and sensors on an autonomous vehicle, inputting frames of the driving video and the sensing information into a scene code assigning module to generate scene codes including information on scenes in the frames and on driving events; and (b) at least one of selecting a part of the frames, whose object detection information satisfies a condition, as specific frames by using the scene codes and the object detection information and selecting a part of the frames, matching a training policy, as the specific frames by using the scene codes and the object detection information, and storing the specific frames and specific scene codes in a frame storing part. | 2021-10-28 |
20210334653 | HYBRID DEEP LEARNING SCHEDULING METHOD FOR ACCELERATED PROCESSING OF MULTI-AMI DATA STREAM IN EDGE COMPUTING - Disclosed is a hybrid deep learning scheduling method for accelerated processing of a multi-advanced metering infrastructure (AMI) data stream in edge computing, wherein a skewed data distribution change, which occurs in AMI data, is detected, an edge server computes an online gradient, which is comparatively quickly computed, on the basis of the detected change, a cloud server computes a normalized gradient, which requires a large amount of computation, according to selection by a hybrid scheduler, and the hybrid scheduler performs a total of three operations: (1) a data stream distribution profiling operation, (2) a memory buffer update operation, and (3) a hybrid scheduling operation. | 2021-10-28 |
20210334654 | METHODS AND SYSTEMS FOR REDUCING BIAS IN AN ARTIFICIAL INTELLIGENCE MODEL - Embodiments provide methods and systems for reducing bias in an artificial intelligence model. A method includes computing, by a processor, a reward value based at least in part on a similarity between model predictions from a pre-trained model and agent predictions from a Reinforcement Learning (RL) agent. The method includes performing each step of one or more steps of a rule of a plurality of rules. The rule is assigned a weight and the rule includes a protected attribute, a cumulative statistic value type, and a comparison threshold. The method includes sending a cumulative reward value generated using the reward value and each weighted punishment value computed based at least in part on applying each rule of the plurality of rules to the RL agent. The RL agent learns to biases from the agent predictions while maintaining similarity with model predictions by maximizing the cumulative reward value. | 2021-10-28 |
20210334655 | PREDICTING PROPERTIES OF MATERIALS FROM PHYSICAL MATERIAL STRUCTURES - Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for predicting one or more properties of a material. One of the methods includes maintaining data specifying a set of known materials each having a respective known physical structure; receiving data specifying a new material; identifying a plurality of known materials in the set of known materials that are similar to the new material; determining a predicted embedding of the new material from at least respective embeddings corresponding to each of the similar known materials; and processing the predicted embedding of the new material using an experimental prediction neural network to predict one or more properties of the new material. | 2021-10-28 |
20210334656 | COMPUTER-IMPLEMENTED METHOD, COMPUTER PROGRAM PRODUCT AND SYSTEM FOR ANOMALY DETECTION AND/OR PREDICTIVE MAINTENANCE - An example method comprises receiving a new observation characterizing at least one parameter of an entity; inputting the new observation to a deep neural network having hidden layers; obtaining a second set of intermediate output values that are output from at least one of the hidden layers by inputting the received new observation to the deep neural network; mapping the second set of intermediate output values to a second set of projected values; determining whether or not the received new observation is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values, calculating a prediction for the new observation; and determining a result indicative of the occurrence of at least one anomaly in the entity based on the prediction and the determination whether or not the new observation is an outlier. | 2021-10-28 |
20210334657 | COGNITIVE COMPUTING METHODS AND SYSTEMS BASED ON BILOGIVAL NEUROL NETWORKS - A Biological Neural Network (BNN) core unit comprising a neural cell culture, an input stimulation unit, an output readout unit may be controlled through its various life cycles to provide data processing functionality. An automation system comprising an environmental and chemical controller unit adapted to operate with the BNN stimulation and readout data interfaces facilitates the monitoring and adaptation of the BNN core unit parameters. Pre-processing and post-processing of the BNN interface signals may further facilitate the training and reinforcement learning by the BNN. Multiple BNN core units may also be assembled together as a stack. The proposed system provides a BNN Operating System as a core component for a wetware server to receive, process and transmit data for different client applications without exposing the BNN core unit components to the client user while requiring significantly less energy than conventional silicon-based hardware and software information processing for high-level cognitive computing tasks. | 2021-10-28 |
20210334658 | METHOD FOR PERFORMING CLUSTERING ON POWER SYSTEM OPERATION MODES BASED ON SPARSE AUTOENCODER - The present disclosure provides a method for performing clustering on operation modes of a power system based on a sparse autoencoder. The method includes: obtaining related data of the power system; setting a training parameter, a number of hidden layers, and a number of neurons; training an autoencoder model using the related data and extracting a topological structure and a weight matrix from the model; performing cluster analysis to obtain a number of typical scenarios; and performing decoding to obtain original data at centers of respective scenarios. The present disclosure can achieve fast selection and dimensionality reduction of feature vectors representing operation modes of a power system. In view of this, the present disclosure provides a novel idea and method for selecting a feature vector representing an operation mode of a power system and generating a typical operation scenario. | 2021-10-28 |
20210334659 | METHOD AND APPARATUS FOR ADVERSARIAL TRAINING OF MACHINE LEARNING MODEL, AND MEDIUM - The present application discloses a method and an apparatus for adversarial training of a machine learning (ML) model and a medium. The method includes: obtaining input information in a training sample; extracting features of a plurality of input characters in the input information; inputting the features of the plurality of input characters to the ML model, to capture an attention weight on an input character of the plurality of input characters by an attention layer of the ML model; disturbing the attention weight captured by the attention layer, so that the ML model outputs a predicted character according to the attention weight disturbed; and training the ML model according to a difference between the predicted character and a labeled character in the training sample. | 2021-10-28 |
20210334660 | TECHNOLOGY FOR ANALYZING SENSOR DATA TO DETECT CONFIGURATIONS OF VEHICLE OPERATION - Systems and methods for using collecting and analyzing device sensor data to determine whether an individual is an operator or a passenger of a vehicle are disclosed. According to certain aspects, an electronic device associated with the individual may collect or access sensor data that is indicative of or associated with an operation of the vehicle. The electronic device may transmit pertinent portion(s) of the sensor data to a backend server, which may input the portion(s) into a neural network for analysis. The neural network may output a probability metric(s) indicative of whether the individual is a passenger or an operator of the vehicle. | 2021-10-28 |
20210334661 | IMAGE PROCESSING METHOD AND APPARATUS BASED ON SUPER NETWORK, AND COMPUTER STORAGE MEDIUM - The present disclosure relates to an image processing method and apparatus based on a super network, and a computer storage medium. The method can include that a pretrained backbone network is merged with a rear end of a target detection network to obtain a merged super network, the merged super network is trained, Neural Architecture Search (NAS) is performed based on the trained super network to obtain a target detection neural architecture, and an image to be processed is processed by using the target detection neural architecture to obtain an image processing result. | 2021-10-28 |
20210334662 | METHOD FOR LEARNING NEUARAL NETWORK AND DEVICE THEREOF - A method of operating a neural network device including a plurality of layers, includes receiving sensing data from at least one sensor, determining environmental information, based on the received sensing data, determining multiple layers corresponding to the determined environmental information, and dynamically reconstructing the neural network device by changing at least two layers, among the plurality of layers, to the determined multiple layers. | 2021-10-28 |
20210334663 | COMPILER-BASED METHOD FOR FAST CNN PRUNING VIA COMPOSABILITY - The present disclosure describes various embodiments of methods and systems of training a pruned neural network. One such method comprises defining a plurality of tuning blocks within a neural network, wherein a tuning block is a sequence of consecutive convolutional neural network layers of the neural network; pruning at least one of the plurality of tuning blocks to form at least one pruned tuning block, and pre-training the at least one pruned tuning block to form at least one pre-trained tuning block. The method further comprises assembling the at least one pre-trained tuning block with other ones of the plurality of tuning blocks of the neural network to form a pruned neural network; and training the pruned neural network, wherein the at least one pre-trained tuning block is initialized with weights resulting from the pre-training of the at least one pruned tuning block. Other methods and systems are also provided. | 2021-10-28 |
20210334664 | Domain Adaptation for Machine Learning Models - Adapting a machine learning model to process data that differs from training data used to configure the model for a specified objective is described. A domain adaptation system trains the model to process new domain data that differs from a training data domain by using the model to generate a feature representation for the new domain data, which describes different content types included in the new domain data. The domain adaptation system then generates a probability distribution for each discrete region of the new domain data, which describes a likelihood of the region including different content described by the feature representation. The probability distribution is compared to ground truth information for the new domain data to determine a loss function, which is used to refine model parameters. After determining that model outputs achieve a threshold similarity to the ground truth information, the model is output as a domain-agnostic model. | 2021-10-28 |
20210334665 | TEXT-BASED EVENT DETECTION METHOD AND APPARATUS, COMPUTER DEVICE, AND STORAGE MEDIUM - A training method includes obtaining a first data set and a second data set, each of the first data set and the second data set including event instances, the event instances include text and events corresponding to the text. The training method also includes training an adversarial network using the first data set and the second data set, the adversarial network includes processing circuitry configured as a generator and a discriminator. The discriminator is configured to output first reliable probabilities of the event instances in the first data set, and second reliable probabilities of the event instances inputted by the generator. A loss function of the adversarial network is used to adjust a parameter of the adversarial network, to maximize the first reliable probabilities and minimize the second reliable probabilities. The method further includes obtaining, by the trained adversarial network, a reliable event instance in the second data set. | 2021-10-28 |
20210334666 | MACHINE LEARNING-BASED INFERENCE OF GRANULAR FONT PROPERTIES - A textual properties model is used to infer values for certain font properties of interest given certain text-related data, such as rendered text images. The model may be used for numerous purposes, such as aiding with document layout, identifying font families that are similar to a given font families, and generating new font families with specific desired properties. In some embodiments, the model is trained from a combination of synthetic data that is labeled with values for the font properties of interest, and partially-labeled data from existing “real-world” documents. | 2021-10-28 |
20210334667 | OPTIMIZING GRADIENT BOOSTING FEATURE SELECTION - Gradient Boosting Decision Tree (GBDT) successively stacks many decision trees which at each step try to fix the residual errors from the previous steps. The final score produced by the GBDT is the sum of the individual scores obtained by the decision trees for an input vector. Overfitting in GBDT can be reduced by removing the input values that have the least impact on the output from the training data. One way to determine which input variable has the lowest predictive value is to determine the input variable that is used for the first time in the latest decision tree in the GBDT. This method of identifying the low-predictive features to be removed does not require that earlier trees be regenerated to generate the new GBDT. Since the removed feature was already not used in the earlier trees, those trees already ignore the removed feature. | 2021-10-28 |
20210334668 | LOCALITY-AWARE COMPRESSOR-DECOMPRESSOR FOR KEEPING PREDICTION MODELS UP-TO-DATE IN RESOURCE CONSTRAINED NETWORKS - A global prediction manager for generating predictions using data from data zones includes storage for storing a model repository comprising a global model set and a prediction manager. The prediction manager obtains a local model set from a data zone of the data zones indicating that the global model set is unacceptable; makes a determination that the local model set is acceptable; in response to the determination: distributes the local model set to at least one second data zone of the data zones; obtains compressed telemetry data, that was compressed using the local model set, from the data zone and the at least one second data zone; and generates a global prediction regarding a future operating condition of the data zones using: the compressed local telemetry data and the local model set. | 2021-10-28 |
20210334669 | METHOD, APPARATUS, DEVICE AND STORAGE MEDIUM FOR CONSTRUCTING KNOWLEDGE GRAPH - A method, apparatus, device, and storage medium for constructing a knowledge graph, relates to the field of data processing, and specifically to artificial intelligence technology is provided. The method may include: determining a scene and a scene element of the scene; determining a target tag from attribute tags based on an association relationship between an entity and the scene element, and an association relationship between the entity and each of the attribute tags; and establishing an edge between a scene node and a target tag node, to obtain a knowledge graph including scene information. | 2021-10-28 |
20210334670 | METHOD AND SYSTEM FOR SEMI-AUTOMATED GENERATION OF MACHINE-READABLE SKILL DESCRIPTIONS OF PRODUCTION MODULES - Production logs and industrial ontologies are processed with an inductive logic program performing class expression learning in order to create class expressions, with each class expression representing a constraint or property of a skill of a production module. The resulting class expressions are ordered by a metric to form an ordered recommender list and displayed to a user for postprocessing. The user selects suitable class expressions from the ordered recommender list, so that the system can build a machine-readable skill description with the selected class expressions. This approach to generating formal, machine-readable skill descriptions minimizes the labor time and domain expertise needed to equip production modules with their skill description. Selecting the correct class expression from the automatically generated ordered recommender list is a much lower effort than manual labeling from scratch. | 2021-10-28 |
20210334671 | Learning Agent - A digital computational learning system and corresponding method plan a series of actions to accomplish tasks. The system learns, automatically, a plurality of actor perceiver predictors (APP) nodes. Each APP node is associated with a context, action, and result. The result is expected to be achieved in response to the action being taken as a function of the context having been satisfied. Each APP node is associated with an action-controller that includes an instance of a planner that includes allied planners. The action-controller is associated with a goal state and employs the allied planners to determine a sequence of actions for reaching the goal state. The allied planners enable the system to plan a series of actions to accomplish complex tasks in a manner that is more robust and resilient relative to current state of the art artificial intelligence based learning systems and methods. | 2021-10-28 |
20210334672 | Computer-Implemented, User-Controlled Method of Automatically Organizing, Storing, and Sharing Personal Information - A computer-implemented infrastructure providing a consistent graphical user interface that supports user-controlled organizing, storing, accessing and sharing of heterogeneous personal information of a specific user uses computer processes executed by a server system. The computer processes include receiving a set of items of information from a computing device operated on behalf of the specific user; for each item of information in the received set of items, obtaining, as a result of parsing the received set of items, new information; feeding to an artificial intelligence engine the new information, and other user information stored in association with an internal account of the specific user, in order to produce derived information; and storing the new information and the derived information, in a storage system in communication with the server system, in an encrypted format, and associating such stored item of information with an internal account of the specific user; wherein the stored items of information are made accessible only in accordance with permissions controlled by the specific user. | 2021-10-28 |
20210334673 | ONLINE UNSUPERVISED ANOMALY DETECTION - A computerized-method for real-time detection of anomalous data, by processing high-speed streaming data. In a computerized-system receiving a data-stream comprised of unlabeled data points, and operating an Anomalous Data Detection (ADD) module. The ADD module receives at least one of: (i) k number of data point neighbors for each data point; (ii) X number of data points in a predetermined period of time; (iii) d number of dimensions of each data point, threshold; and (iv) n number of data points that said ADD module is operating on, in a predefined time unit. Then, the ADD module prepares a dataset having n data points from the received X data points; and then identifies one or more data points, from the received data stream, as outliers to send an alert with details related to the identified outliers, thus, dynamically evaluating local outliers in the received data stream. | 2021-10-28 |
20210334674 | USING MACHINE LEARNING TO DETERMINE JOB FAMILIES USING JOB TITLES - A system and method are disclosed for training a machine learning model using information pertaining to job titles. Training data for the machine learning model is generated. Generating the training data includes generating first training input including information identifying the job titles associated with organizations, and generating a first target output for the first training input. The first target output identifies an indication of job families. A job family identifies a category of personnel positions that are categorized based on one or more characteristics that are shared between the personnel positions of the category. The training data to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including the first target output is provided. | 2021-10-28 |
20210334675 | AUXILIARY POWER UNIT USAGE PREDICTION - A system includes a machine-learning device configured to receive data associated with an aircraft. The data includes a flying time associated with one or more flight phases of travel to a destination airport, temperature data associated with one or more of the flight phases, and a number of occupants on board the aircraft. The machine-learning device is configured to process the data to generate prediction data regarding usage of an auxiliary power unit of the aircraft and to generate a message based on the prediction data. The message indicates at least one of an estimated number of on/off events of the auxiliary power unit or an estimated duration of use of the auxiliary power unit. | 2021-10-28 |
20210334676 | SYSTEM AND METHOD FOR DIFFERENTIAL TESTING OF EVOLVING RULES - A computer implemented method for testing rules by a computing device including selecting a current version of a rule and prior version of the rule, comparing the prior version of the rule and the current version of the rule to each other to identify a type of change made in the current version of the rule with respect to the prior version of the rule, and testing the prior version of a rule and the current version of the rule using a common data set, the testing being based on the identified type of change. The test result is provided to a user. | 2021-10-28 |
20210334677 | EFFICIENCY DRIVEN DATA COLLECTION AND MACHINE LEARNING MODELING RECOMMENDATION - A machine learning assessment system is provided. The system identifies multiple datasets and multiple machine learning (ML) modeling algorithms based on the client profile. The system assesses a cost of data collection for each dataset of the multiple datasets. The system assesses a performance metric for each ML modeling algorithm of the multiple modeling algorithms. The system recommends a dataset from the multiple datasets and an ML modeling algorithm from the multiple ML modeling algorithm based on the assessed costs of data collection for the multiple datasets and the assessed performance metrics for the multiple ML modeling algorithms. | 2021-10-28 |
20210334678 | FRAMEWORK FOR MEASURING TELEMETRY DATA VARIABILITY FOR CONFIDENCE EVALUATION OF A MACHINE LEARNING ESTIMATOR - A deployment manager includes storage for storing a prediction model based on telemetry data from the deployments and a prediction manager. The prediction manager generates, using the prediction model and second telemetry data obtained from a deployment of the deployments: a prediction, and a prediction error estimate; in response to a determination that the prediction indicates a negative impact on the deployment: generates a confidence estimation for the prediction based on a variability of the second telemetry data from the telemetry data; in response to a second determination that the confidence estimation indicates that the prediction error estimate is inaccurate: remediates the prediction based on the variability to obtain an updated prediction; and performs an action set, based on the updated prediction, to reduce an impact of the negative impact on the deployment. | 2021-10-28 |
20210334679 | BRAIN OPERATING SYSTEM INFRASTRUCTURE - Embodiments may provide an intelligent adaptive system that combines input data types, processing history and objectives, research knowledge, and situational context to determine the most appropriate mathematical model, choose the computing infrastructure, and propose the best solution for a given problem. For example, a method may comprise receiving data relating to a problem to be solved, generating a description of the problem, wherein the description conforms to defined format, obtaining at least one machine learning model relevant to the problem, selecting, at the computer system, computing infrastructure upon which to execute the at least one machine learning model relevant to the problem, wherein the selected computing infrastructure comprises a mesh of interconnected micro-applications including at least some deep cognitive neural networks, and executing the at least one machine learning model relevant to the problem using the selected computing infrastructure to generate at least one recommendation relevant to the problem. | 2021-10-28 |
20210334680 | OPTIMIZATION APPARATUS, OPTIMIZATION METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM - An optimization apparatus includes a processor. The processor configured to calculate a change amount of energy represented by an evaluation function of a case of changing a state of any one of a plurality of state variables so as to increase or decrease a value by 1 in a case of a state variable taking multiple values, the evaluation function representing the energy including the plurality of state variables, determine whether to set a state change in the state variable as a candidate according to a correlation between a threshold and a total change amount, stochastically determine whether to adopt the state change set as the candidate, calculate post-transition energy after executing a state transition of the state variable according to the state change, and obtain minimum energy by setting the post-transition energy as the minimum energy when the post-transition energy is less than the minimum energy. | 2021-10-28 |
20210334681 | ELECTRONIC DEVICE AND METHOD FOR TURNOVER RATE PREDICTION - An electronic device and a method for turnover rate prediction are provided, wherein the method includes: receiving human resource (HR) data; generating a feature dataset according to the HR data; inputting a first subset of the feature dataset to a first machine learning (ML) model to generate a first prediction; inputting a second subset of the feature dataset to a second ML model to generate a second prediction; and inputting the first prediction, the second prediction, and a third subset of the feature dataset to a third ML model to generate a first turnover rate prediction. | 2021-10-28 |
20210334682 | MACHINE LEARNING SYSTEMS FOR MANAGING INVENTORY - Techniques are disclosed for training a machine learning model to select a route for performing tasks in a target set of inventory tasks. The machine learning model may be trained by obtaining training data sets that include characteristics of previously performed tasks by one or more task performers. Example characteristics may include locations associated with the previously performed tasks, a duration of time taken to perform the previous tasks, a route taken to perform the tasks, a sequence in which tasks a set of tasks were performed, and attributes of the task performers themselves. The machine learning model may be trained using these training data sets and the applied to a received set of target tasks. The trained machine learning model may then generate a route and/or sequence in which the tasks of the target set of tasks may be performed. | 2021-10-28 |
20210334683 | Method for predicting an operating duration of a connected object - A method for predicting an operating duration of a connected object. The method includes: obtaining an amount of instantaneous energy consumed by the connected object; obtaining at least one amount of instantaneous energy dependent on an ambient energy, and able to be used to supply the connected object; obtaining a remaining service life of the connected object on the basis of the amounts; obtaining the predicted operating duration of the connected object on the basis of the remaining service life; and performing at least one action taking into account the predicted operating duration. | 2021-10-28 |
20210334684 | METHOD OF BUILDING AND OPERATING DECODING STATUS AND PREDICTION SYSTEM - A method of building a decoding status prediction system is provided. Firstly, plural read records are collected during read cycles of a flash memory. Then, the plural read records are classified into read records with a first read result and read records with a second read result. Then, a first portion of the read records with the first read result are divided into K0 groups according to a clustering algorithm, and a second portion of the read records with the second read result are divided into K1 groups according to the clustering algorithm. Then, the read records of the K0 groups and the K1 groups are used to train prediction models. Consequently, K0×K1 prediction models are generated. Then, the prediction models are combined as a prediction database. | 2021-10-28 |
20210334685 | SYSTEM AND PROCESS FOR VERIFYING POWDER BED FUSION ADDITIVE MANUFACTURING OPERATION AS BEING DEFECT FREE - A method of evaluating an additive manufacturing process includes receiving a set of additive manufacturing parameters and an additive manufacturing part design at an analysis module, receiving a set of random values at the analysis module, determining a probability distribution of stochastic flaws within a resultant additively manufactured article using at least one multidimensional space physics model, and categorizing the additive manufacturing part design as defect free when the probability distribution is below a predefined threshold. Each value in the set of random values corresponds to a distinct variable in a set of variables. Each variable in the set of variables at least partially defines at least one of an uncontrolled additive manufacturing parameter and an uncontrollable additive manufacturing parameter. | 2021-10-28 |
20210334686 | SYSTEMS AND METHODS FOR SHORT IDENTIFIER BEHAVIORAL ANALYTICS - Embodiments relate to computing systems, methods, and non-transitory computer-readable storage media. Identifier collisions are determined in historical event data based on short identifiers associated with a first event location and short identifiers associated with a second event location geographically dispersed from the first event location. Behavior analytics are performed on short identifiers in the historical event data to generate behavioral models associated with the short identifiers. Adjusted behavioral models are generated based on the determined identifier collisions. A short identifier is obtained from a client device, and based on the short identifier, an adjusted behavioral model of the adjusted behavioral models is determined. An expected event frequency is determined based on the adjusted behavioral model and an actual event frequency is determined. An indication of a first new event value is transmitted to the client device based on a determination that the expected event frequency is less than the actual event frequency, whereby the client device displays a notification corresponding to the indication of the first new event value. | 2021-10-28 |
20210334687 | AUTONOMOUS CORRECTION OF COURSE OF ACTION - Discussed herein are devices, systems, and methods for autonomous, dynamic course of action (COA) generation and management. A method can include issuing a communication to one or more assets indicating operations of a first COA to be performed, receiving, by an intelligence, surveillance, and reconnaissance (ISR) device, data indicating an unexpected event, not accounted for in the first COA, has occurred, in response to the data indicating the unexpected event, identifying a second COA or a portion of a second COA that satisfies a mission of the first COA and accounts for the unexpected event, and issuing a second communication to the one or more assets indicating one or more operations of the second COA to be performed. | 2021-10-28 |
20210334688 | FAULT-TOLERANT QUANTUM CAT STATE PREPARATION - A quantum computing system is adapted to prepare a cat state in a quantum circuit with fault tolerance t and circuit depth less than or equal to 4+4t by performing a series of operations that includes: performing a sequence of joint parity measurements on individual pairs of neighboring qubits in a series of qubits entangled to form an initial cat state; repeating the sequence of measurements over at least t-rounds; and disentangling a first set of alternating qubits from the initial cat state, the prepared cat state being formed by a remaining second set of alternating qubits, the second set of alternating qubits being interlaced with the first set of alternating qubits along a line of one-dimensional connectivity, the series of operations being sufficient to guarantee that a prepared cat state is has less than or equal to t number of faults. | 2021-10-28 |
20210334689 | OPTIMIZING QUBIT OPERATING FREQUENCIES - Methods, systems, and apparatus for determining frequencies at which to operate interacting qubits arranged as a two dimensional grid in a quantum device. In one aspect, a method includes the actions of defining a first cost function that characterizes technical operating characteristics of the system. The cost function maps qubit operation frequency values to a cost corresponding to an operating state of the quantum device; applying one or more constraints to the defined first cost function to define an adjusted cost function; and adjusting qubit operation frequency values to vary the cost according to the adjusted cost function such that the operating state of the quantum device is improved. | 2021-10-28 |
20210334690 | METHODS AND SYSTEMS FOR TENSOR NETWORK CONTRACTION BASED ON HYPERGRAPH DECOMPOSITION AND PARAMETER OPTIMIZATION - Methods and systems for tensor network contraction are provided. A method implemented by a computing host includes obtaining a plurality of tensor nodes associated with a tensor network and a plurality of indices respectively associated with the plurality of tensor nodes; generating a graph associated with the tensor network, wherein the plurality of tensor nodes correspond to a plurality of vertices of the graph and the plurality of indices correspond to a plurality of edges of the graph, respectively; decomposing the graph into a plurality of sub-graphs; and for each sub-graph of the plurality of sub-graphs, iteratively decomposing a current sub-graph into a plurality of next-tier sub-graphs until a size of each of the plurality of next-tier sub-graphs is less than a pre-set threshold. | 2021-10-28 |
20210334691 | ERROR CORRECTED VARIATIONAL ALGORITHMS - Methods, systems and apparatus for approximating a target quantum state. In one aspect, a method for determining a target quantum state includes the actions of receiving data representing a target quantum state of a quantum system as a result of applying a quantum circuit to an initial quantum state of the quantum system; determining an approximate quantum circuit that approximates the specific quantum circuit by adaptively adjusting a number of T gates available to the specific quantum circuit; and applying the determined approximate quantum circuit to the initial quantum state to obtain an approximation of the target quantum state. | 2021-10-28 |
20210334692 | SECURE PROBABILISTIC ONE-TIME PROGRAM BY QUANTUM STATE DISTRIBUTION - Method and system for executing a one-time program comprising at least one instruction operating on at least one input value (a, b) and returning at least one output value (O), wherein each instruction of the one-time program is encoded onto a state of an elementary quantum system, comprising: encoding the at least one input value (a, b) onto a quantum gate according to a pre-defined input-encoding scheme; applying the quantum gate to the at least one elementary quantum system; making a measurement of a resulting state of the at least one elementary quantum system after the quantum gate; and determining the at least one output value from a result of the measurement. | 2021-10-28 |
20210334693 | AUTOMATED GENERATION OF EXPLAINABLE MACHINE LEARNING - A computer-implemented method and system are provided to perform a machine learning pipeline process to produce an explainable machine learning model. A computing device may be configured to train a plurality of machine learning models with a set of respective feature datasets to generate an accuracy and explainability property for each trained model. The computing device may evaluate a plurality of the trained machine learning models and select a model as an explainable machine learning model based on at least one of the accuracy and the explainability property. | 2021-10-28 |
20210334694 | PERTURBED RECORDS GENERATION - Reducing a count of perturbed records in a machine learning dataset by application of a correlation matrix of feature values identified in training records to reduce the number of features represented in the perturbed records. Deleting one of a pair of correlated records is achieved with reference to a correlation score that identifies features of sufficient similarity to be paired up. Reducing the number of features for which values are assigned in a data perturbation process results in a relatively reduced number of perturbed records. | 2021-10-28 |
20210334695 | SYSTEM TO CORRECT MODEL DRIFT IN MACHINE LEARNING APPLICATION - A model correction tool automatically detects and corrects model drift in a model for a machine learning application. To detect drift, the tool continuously monitors input data, outputs, and/or technical resources (e.g., processor, memory, network, and input/output resources) used to generate outputs. The tool analyzes changes to input data, outputs, and/or resource usage to determine when drift has occurred. When drift is determined to be occurring, the tool retrains a model for a machine learning application. | 2021-10-28 |
20210334696 | TRAINING REINFORCEMENT MACHINE LEARNING SYSTEMS - A method of training a reinforcement machine learning computer system. The method comprises providing a machine-learning computer programming language including a pre-defined plurality of reinforcement machine learning criterion statements, and receiving a training specification authored in the machine-learning computer programming language. The training specification defines a plurality of training sub-goals with a corresponding plurality of the reinforcement machine learning criterion statements supported by the machine-learning computer programming language. The method further comprises computer translating the plurality of training sub-goals from the training specification into a shaped reward function configured to score a reinforcement machine learning model configuration with regard to the plurality of training sub-goals. The method further comprises running a training experiment with the reinforcement machine learning model configuration, scoring the reinforcement machine learning model in the training experiment with the shaped reward function, and adjusting the reinforcement machine learning model configuration based on the shaped reward function. | 2021-10-28 |
20210334697 | Artificial Intelligence Recommendation System - Various embodiments of the present disclosure facilitate recommendation prediction using machine learning. In one example, an embodiment provides for generating embeddings data related to one or more provider entities, predicting a set of provider entities for a patient entity based on a provider machine learning model, ranking provider entities in the set of provider entities to generate a ranked set of provider entities, and performing one or more actions to provide a recommendation for the patient entity based on the ranked set of provider entities. | 2021-10-28 |
20210334698 | CONSTRUCTING MACHINE LEARNING MODELS - An example method includes initializing a configuration file for a machine learning model, wherein the initializing is performed in response to receiving a request from a user, and wherein the configuration file comprises a plurality of sections that is configurable by the user, configuring at least one parameter of a feature engineering rules section of the configuration file, wherein the configuring the at least one parameter of the feature engineering rules section is based on a first value provided by the user, configuring at least one parameter of an algorithm definitions section of the configuration file, wherein the configuring the at least one parameter of the algorithm definitions section is based on a second value provided by the user, and populating the configuration file using the feature engineering rules section as configured and the algorithm definitions section as configured, to generate the machine learning model. | 2021-10-28 |
20210334699 | USING MACHINE LEARNING TO VIRTUALIZE PRODUCT TESTS - Systems and methods for using machine learning models to predict an outcome of a product test are described. According to certain aspects, an electronic device may generate a machine learning model using training data indicating a set of results of a set of products tested according to a specific product test. The electronic device may access a set of inputs comprising a set of additional results of at least one additional product tested according to a modified product test, and input the set of inputs into the machine learning model. Based on an output of the machine learning model, the electronic device may predict whether the at least one additional product would pass or comply with the original specific product test. | 2021-10-28 |
20210334700 | SYSTEM AND METHOD OF CREATING ARTIFICIAL INTELLIGENCE MODEL, MACHINE LEARNING MODEL OR QUANTUM MODEL GENERATION FRAMEWORK - Systems and methods for generating at least one of an automated machine learning (ML) model, artificial intelligence (AI) model or quantum ML model for a user via a model generation framework are provided. The method includes receiving a user input including at least one of a data, one or more tasks and a metadata, from the user, the metadata including least one of: a selection of domain, a selection of sub-domain, or one or more keyword tags. One or more building blocks are determined in the selection of domain or said selection of sub-domain by performing a meta-learning, a transfer learning or a neural architecture search. An optimal model is iteratively determined based on the building blocks and a performance estimation of the building blocks, the optimal model including at least one of AI model, ML model or quantum ML model. The optimal model is rendered to the user. | 2021-10-28 |
20210334701 | MACHINE LEARNING METHOD - A machine learning method is provided, including: obtaining training data, where the training data includes a training feature, training labels, and a training weight; inputting the training data to a first machine learning model, where the first machine learning model has first model data, the first model data includes a first model feature, first model labels, and first model weights, and the first model labels correspond to the first model weights in a one-to-one manner; and training the first machine learning model by using a training step to obtain a second machine learning model. The training step includes: when the first model feature matches the training feature, and one of the first model labels is the same as any of the training labels, adjusting the first model weight corresponding to the first model label that is the same as any of the training labels according to the training weight. | 2021-10-28 |
20210334702 | MODEL EVALUATING DEVICE, MODEL EVALUATING METHOD, AND PROGRAM - A model evaluating device is configured to evaluate performance of a prediction model configured to generate predicted values of a target variable for an explanatory variable. The model evaluating device includes a generating unit configured to generate expanded MR data by transforming evaluation data, and an evaluating unit configured to evaluate the performance of the prediction model based on a first predicted value generated by the prediction model based on the evaluation data, and a second predicted value generated by the prediction model based on the MR data. | 2021-10-28 |
20210334703 | METHODS AND SYSTEMS CONFIGURED TO SPECIFY RESOURCES FOR HYPERDIMENSIONAL COMPUTING IMPLEMENTED IN PROGRAMMABLE DEVICES USING A PARAMETERIZED TEMPLATE FOR HYPERDIMENSIONAL COMPUTING - A method of defining an implementation of circuits in a programmable device can be provided by receiving a plurality of specifications for a hyperdimensional (HD) computing machine learning application for execution on a programmable device, determining parameters for a template architecture for HD computing machine learning using the plurality of specifications, the template architecture including an HD hypervector encoder, an HD associative search unit, programmable device pre-defined processing units, and programmable device pre-defined processing elements within the pre-defined processing units, and generating programmable device code configured to specify resources to be allocated within the programmable device using pre-defined circuits defined for use in the programmable device using the determined parameters for the template architecture. | 2021-10-28 |
20210334704 | Method and System for Operating a Technical Installation with an Optimal Model - A method for operating a technical installation with an optimal model, wherein the installation forms part of a system with a first technical installation and at least one second technical installation, where each installation includes a control apparatus and a connected technical device, and where the system also includes a server with a memory. | 2021-10-28 |
20210334705 | PERSISTED MACHINE LEARNING-BASED SENSING OF RF ENVIRONMENTS - A RF Environment Learning Module includes a machine learning/artificial intelligence sensing controller process that schedules feature vector extractors and one or more machine learning (ML) signal classifiers in order to classify received radio signals and a ML-based validation process that reasons over the classification results to determine if they are valid or reasonable. | 2021-10-28 |
20210334706 | AUGMENTATION DEVICE, AUGMENTATION METHOD, AND AUGMENTATION PROGRAM - An augmentation apparatus ( | 2021-10-28 |
20210334707 | SYSTEM AND METHOD FOR MANAGING CLASSIFICATION OUTCOMES OF DATA INPUTS CLASSIFIED INTO BIAS CATEGORIES - A method includes receiving, by a processor, bias data categories. A data input from a user for classification in data categories is received. A classification machine learning model is utilized to classify the data input in at least one data category and determine a first confidence probability in a classification outcome. A bias filter machine learning model is utilized to determine a second confidence probability that the classification outcome of classifying the data input into the at least one data category is based on at least one bias characteristic associated with at least one bias data category. A gate machine learning model is utilized to determine when to output the classification outcome of classifying the data input into the at least one data category to a computing device of a user based at least in part on the first confidence probability, the second confidence probability, and a predefined bias threshold. | 2021-10-28 |
20210334708 | Method and System of Utilizing Unsupervised Learning to Improve Text to Content Suggestions - Method and system for training a text-to-content suggestion ML model include accessing a dataset containing unlabeled training data collected from an application, the unlabeled training data being collected under user privacy constraints, applying an ML model to the dataset to generate a pretrained embedding, and applying a supervised ML model to a labeled dataset to train the text-to-content suggestion ML model utilized by the application by utilizing the pretrained embedding generated by the supervised ML model. | 2021-10-28 |
20210334709 | BREADTH-FIRST, DEPTH-NEXT TRAINING OF COGNITIVE MODELS BASED ON DECISION TREES - The present invention is notably directed to a computer-implemented method of training a cognitive model. The cognitive model includes decision trees as base learners. The method is performed using processing means to which a given cache memory is connected, so as to train the cognitive model based on training examples of a training dataset. The cognitive model is trained by running a hybrid tree building algorithm, so as to construct the decision trees and thereby associate the training examples to leaf nodes of the constructed decision trees, respectively. The hybrid tree building algorithm involves a first routine and a second routine. Each routine is designed to access the cache memory upon execution. The first routine involves a breadth-first search tree builder, while the second routine involves a depth-first search tree builder. | 2021-10-28 |
20210334710 | CAR-HAILING METHOD AND DEVICE, AS WELL AS COMPUTER READABLE STORAGE MEDIUM - A car-hailing method and device, as well as a computer readable storage medium. The method includes receiving a car-hailing instruction and sending a car-hailing notification to a second terminal associated with a first terminal so that the second terminal sends car-hailing information to a target object according to the car-hailing notification. The car-hailing information includes at least a geographical location of the first terminal, user information and a destination. | 2021-10-28 |
20210334711 | SYSTEM AND METHODS FOR CONNECTING CONTENT PROMOTERS AND ARTISTS FOR CONTENT PROMOTION TRANSACTIONS - A system and methods for connecting content promoters and artists to facilitate integration of an artist's content into a promoter's performance at an event. The system allows a promoter to publish available slots in an event that might be filled with an artist's content. The system provides a user interface that allows promoters to specify parameters that characterize available slots at the event. The parameters may provide details about the event, details about the slots for which the promoter will accept requests, the characteristics of the content the promoter would prefer to play in an available slot, and prices that the promoter charges for content to be played during the slot. Once the promoter provides the system the details of the available slots, the system allows the promoter to publish the event/slots and make the published events/slots available for review by artists. | 2021-10-28 |
20210334712 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY STORAGE MEDIUM - The present disclosure enables equipment not provided in a company vehicle to be rented out to a user together with the company vehicle. An information processing apparatus of the present disclosure includes a controller that is configured to acquire a use application to privately use a company vehicle that is used by the company for business activity, from a user who is an employee of a company, specify equipment that is able to be rented out together with the company vehicle to be rented out to the user and that is not provided in the company vehicle, as recommended equipment, based on the use application, and supply guide information suggesting rental of the recommended equipment to a user terminal. | 2021-10-28 |
20210334713 | Resource Capacity Planning System - A system is provided that generates a capacity plan for a resource representing supply to meet demand based on minimizing a cost objective. The system generates demand scenarios by applying a stochastic process that factors in historical information, future goals, and uncertainty in demand. The system generates supply scenarios indicating supply over time for the resource by applying a stochastic process that factors in factors relating to quantity of supply units of the resource and uncertainty in supply. The system identifies a supply scenario that minimizes costs relating to delivery of supply at times other than the times at which supply is need to meet demand based on the demand scenarios. The supply scenario represents the capacity plan. | 2021-10-28 |
20210334714 | Business Designer - The present disclosure provides systems and methods for a synergetic, multi-interface workflow designer: a visual tool that enables the design, building, and use of high-level processes and standard configuration workflows among multiple users with varying technical capabilities on multiple systems. For example, it enables a line of business user to design and create a high-level process on a first designer interface. The process and its data are received by a second designer interface, which translates the process and data into a standard configuration workflow. A workflow designer may access the second designer interface to create, refine, and finalize the standard configuration workflow based on the data from the first designer interface. | 2021-10-28 |
20210334715 | COMPUTER-BASED SYSTEMS CONFIGURED TO DETECT FRAUDULENT ACTIVITIES RELATED TO CARD-TRANSACTING DEVICES AND METHODS OF USE THEREOF - Systems and methods of detecting fraudulent activity including skimmers adapted to compromise transacting devices such as automated teller machines (ATMs) are disclosed. In one embodiment, an exemplary computer-implemented method may comprise determining that a subject device has a risk level higher than a risk threshold, providing a push notification to a mobile device proximal to the subject device, executing a software application executed by the mobile device for gathering information and transmitting feedback regarding the subject device, and providing an incentive, upon receipt of the feedback, to, for example, an account or device associated with an individual involved with the feedback or interaction with the device. | 2021-10-28 |
20210334716 | INFORMATION PROCESSING DEVICE AND PROGRAM - The problem addressed by the present invention is to enable the presentation of more suitable information to each of construction companies, construction equipment information providers, and oil providers. A computation unit acquires, from an integrated database construction company information acquired by a construction company information acquisition unit, construction equipment information provider information acquired by a construction equipment information provider information acquisition and oil provider information acquired by an oil provider information acquisition unit, and executes the necessary computation. On the basis of the construction company information, the construction equipment information provider information, and the oil provider information, a Web screen generation unit generates information suitable for presentation to the construction companies, construction equipment information providers, and oil providers, and presents that information to the construction companies, construction equipment information providers, and oil providers. Thus, the aforementioned problem is solved. | 2021-10-28 |
20210334717 | Electronic Message Management Program Coordinating Defined Activity And Controlled Recipient/Respondents Through A Unique Id - A system is disclosed that sends, through one or more messaging modalities, an electronic message containing a system or user genera0ted unique identifier to at least one recipient on a system or user enabled and controlled list. The system receives a response electronic message containing the unique identifier and response data identified by a symbol. The program verifies the validity of the unique identifier and optionally that a sender of the response message is on the list. If the unique identifier is verified, the program correlates the message response data with the unique identifier and sends an additional message replicating the response data to the controlled list as a reply or update concerning the message. The program repeats the receiving, verifying, and sending to allow further updates on the message using the modality of communication. | 2021-10-28 |
20210334718 | SYSTEM FOR MANAGING ENTERPRISE DATAFLOWS - A system for managing organization dataflows is disclosed, comprising a database configured to store a plurality of data related to a project scope, personnel, and historical data. An artificial intelligence system receives the plurality of data provide the plurality of data to a machine learning module to determine one or more suggested steps for completing the project. A scheduling module in operable communication with the machine learning module to receive scheduling information and compare the scheduling information with the historical data to determine a timeframe for completing the project for at least one of a plurality of users. | 2021-10-28 |
20210334719 | POWER TRANSMISSION/RECEPTION MANAGEMENT APPARATUS AND COMPUTER-READABLE STORAGE MEDIUM - Provided is a power transmission/reception management apparatus comprising: a storage unit configured to store information indicating a correspondence between a scheduled period during which a vehicle provided with a driving electric power source is enabled to transmit and receive power with a power grid, and the vehicle; and a control unit configured to cause, when the vehicle is not enabled to transmit or receive power with the power grid at a predetermined time point earlier than the scheduled period, a notification to be given to a user correlated with the vehicle. | 2021-10-28 |
20210334720 | System and Method for Multi-Phase Optimization of Haul Truck Dispatch - A system and method for dispatching haul trucks includes a production planner configured to operate based on a production plan. The production planner computationally defines production arcs for transferring material from loading tools to dump sites, computationally develops one or more possible return arcs for each production arc, compiles a set of possible return arcs, and computationally selects a sub-set of the possible return arcs to command a real time dispatcher. | 2021-10-28 |
20210334721 | APPLICATION ENVIRONMENTS FOR ORGANIZING INFORMATION AROUND SPACES AND GOALS - Systems, methods, and software are disclosed herein. A method comprises a computing apparatus displaying, in an application environment, a canvas on which to organize a collection of items on the canvas that are related to a project. The method includes the computing apparatus displaying a new item on the canvas comprising a goal associated with the project and having attributes associated with one or more items native to one or more other application environments. The method also includes the computing apparatus sending one or more requests to express, in the one or more other application environments, an association of the one or more items with the goal. | 2021-10-28 |
20210334722 | SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING MODELS TO DETERMINE SUGGESTED RIDE SHARING OF VEHICLES - A device may generate a shortest path tree based on a passenger starting point identified in driving data. The device may generate a graph with a first layer and a second layer that correspond to the shortest path tree and may add paths from nodes in the first layer to corresponding nodes in the second layer. The device may identify a first shortest path that starts from a driver starting node in the first layer and ends at a passenger end node in the second layer and a second shortest path that starts from the passenger end node and ends at a driver end node in the second layer. The device may calculate a shared cost associated with the driver and the passenger sharing a ride based on the first shortest path and the second shortest path. The device may generate a recommendation based on the shared cost. | 2021-10-28 |
20210334723 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND RECORDING MEDIUM - An information processing apparatus includes a controller that receives, from a terminal of a first user among a plurality of users including a user not belonging to a predetermined organization, a request for use of to a first vehicle associated with the predetermined organization for other than a business activity in the predetermined organization, and registers, in response to the request, a schedule for use of the first vehicle for other than the business activity. Moreover, a user terminal transmits, to a predetermined apparatus, the request for use of first vehicle associated with the predetermined organization for other than the business activity in the predetermined organization from the first user among a plurality of users including a user not belonging to the predetermined organization, and receives, from the predetermined apparatus, in response to the request, a schedule for use of the first vehicle for other than the business activity. | 2021-10-28 |
20210334724 | CONTROL SYSTEM FOR GENERATING AND DISTRIBUTING ENERGY RESOURCES AND OPERATING BUILDING EQUIPMENT - A method for controlling an energy production and distribution system includes identifying sources that supply input resources, subplants that produce output resources using the input resources, and sinks that consume the output resources. The method includes obtaining a cost function including a cost of producing the output resources and generating a transit time constraint that requires the input resources be sent from the sources to the subplants at a first departure time that occurs before a first arrival time at which the input resources are predicted to be used by the subplants. The method includes solving an optimization problem to determine an amount of the output resources to produce at each of multiple time steps within a time period. Solving the optimization problem includes performing an optimization of the cost function subject to the transit time constraint. | 2021-10-28 |
20210334725 | SYSTEM AND METHOD FOR PERFORMING A COMPUTER ASSISTED ORTHOPAEDIC SURGICAL PROCEDURE - A computer assisted surgery system includes a controller configured to display images of the surgical procedure according to a workflow plan. The controller is configured to retrieve data and determine the workflow plan based on the data. The controller may also be configured to record and store data related to the surgical procedure on, for example, a hospital network. | 2021-10-28 |
20210334726 | Automated Evaluation of Refinery and Petrochemical Feedstocks Using a Combination of Historical Market Prices, Machine Learning, and Algebraic Planning Model Information - Computer tool determines target feedstock for a refinery, process complex, or plant. The tool receives a dataset of market conditions and preprocesses the data based on properties of the plant. Using the preprocessed data and machine learning, the tool trains predictive models. Each predictive model calculates a breakeven value of a candidate feedstock for the given plant under an individual market condition. Different predictive models optimize for different market conditions. A trained predictive model is selected based on a current market condition. The tool applies the selected predictive model and determines whether a candidate feedstock is a target feedstock for the refinery under the current market condition. | 2021-10-28 |
20210334727 | FLEET-SPECIFIC PERFORMANCE IMPACT OF VEHICLE CONFIGURATION - Aspects of systems, method, and computer-readable storage media are provided that determine a vehicle configuration based on operator use case-specific performance impacts. A fleet analytics system is deployed as a lightweight, easy-to-use cloud-based service to a non-technical audience. Aspects of the fleet analytics system utilize a telematics system to collect and store real-time data from vehicles, a cloud-computing system to provide as a service that can be used by operators/dealers/sales personnel and that has sufficient complexity to analyze telemetry data, generate a dataset of actual (real-world) use case-related metrics that are related to a target operator's use case, compare the actual metrics against the target operator's metrics, determine an estimated impact of a vehicle configuration variable to the target operator based on the operator's specific use case, and generate a vehicle configuration and visualizations based on the estimated performance impact for communicating the estimated performance impact to the operator. | 2021-10-28 |