37th week of 2022 patent applcation highlights part 52 |
Patent application number | Title | Published |
20220292324 | Dual Interface Metal Cards And Methods Of Manufacturing - (i) Smartcards (SC) manufactured from a web of metal inlays (MI; FIGS. | 2022-09-15 |
20220292325 | RADIO-FREQUENCY IDENTIFICATION CONNECTOR - A radio-frequency identification (RFID) connector, including a connector, including at least one component, and a RFID assembly connected to the connector, the RFID assembly including a RFID tag, and at least one contact arranged on the at least one component and electrically connected to the RFID tag, wherein in an unlocked state of the RFID connector, the RFID tag indicates an open state of the RFID assembly, and in a locked state of the RFID connector, the RFID tag indicates a closed state of the RFID assembly. | 2022-09-15 |
20220292326 | METHOD FOR MANUFACTURING DISPLAY BODY, DISPLAY BODY, AND METHOD FOR VERIFYING AUTHENTICITY OF DISPLAY BODY - A display body includes a base material having a first region, a second region, and a third region. In the display body, the first region is formed with a code or an image of identification information, and the second region is formed with a hidden code containing information obtained by encoding at least a part of the identification information. An encrypted ciphertext is recorded in the third region, and the ciphertext is generated from at least one of the code of the identification information and the hidden code. | 2022-09-15 |
20220292327 | SYSTEMS AND METHODS TO ENHANCE INTERACTIVE ENGAGEMENT WITH SHARED CONTENT BY A CONTEXTUAL VIRTUAL AGENT - Systems and methods are described to enhance interactive engagement during simultaneous delivery of serial or digital content (e.g., audio, video) to a plurality of users. A machine-based awareness of the context of the content and/or one or more user reactions to the presentation of the content may be used as a basis to interrupt content delivery in order to intersperse a snippet that includes a virtual agent with an awareness of the context(s) of the content and/or the one or more user reactions. This “contextual virtual agent” (CVA) enacts actions and/or dialog based on the one or more machine-classified contexts coupled with identified interests and/or aspirations of individuals within the group of users. The CVA may also base its activities on a machine-based awareness of “future” content that has not yet been delivered to the group, but classified by natural language and/or computer vision processing. Interrupting the delivery of content substantially simultaneously to a group of users and initiating dialog regarding content by a CVA enhances opportunities for users to engage with each other about their shared interactive experience. | 2022-09-15 |
20220292328 | CONVOLUTIONAL ARTIFICIAL NEURAL NETWORK BASED RECOGNITION SYSTEM IN WHICH REGISTRATION, SEARCH, AND REPRODUCTION OF IMAGE AND VIDEO ARE DIVIDED BETWEEN AND PERFORMED BY MOBILE DEVICE AND SERVER - The present invention relates to a convolutional artificial neural network-based image and video recognition system, and a method therefor. The recognition system comprises: a mobile device for performing lower layer analysis, transmitting a user question to a server, receiving an answer thereto from the server, and managing the same; the server connected to the mobile device over a network so as to perform data processing in respective neural network layers corresponding to middle and upper layers of a convolutional artificial neural network, and register, analyze, search for, and classify a particular object (image) and video; and a retriever for comparing an FC layer value for artificial neural network processing of an image and video transmitted by the mobile device with an FC layer value for artificial neural network processing of an image and video registered by a content provider. | 2022-09-15 |
20220292329 | NEURAL ARCHITECTURE SEARCH WITH WEIGHT SHARING - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting a neural network to perform a particular machine learning task while satisfying a set of constraints. | 2022-09-15 |
20220292330 | GENERATION AND APPLICATION OF LOCATION EMBEDDINGS - Implementations are described herein for generating location embeddings that capture spatial dependence and heterogeneity of data, making the embeddings suitable for downstream statistical analysis and/or machine learning processing. In various implementations, a position coordinate for a geographic location of interest may be processed using a spatial dependence encoder to generate a first location embedding that captures spatial dependence of geospatial measure(s) for the geographic location of interest. The position coordinate may also be processed using a spatial heterogeneity encoder to generate a second location embedding that captures spatial heterogeneity of the geospatial measure(s) for the geographic location. A combined embedding corresponding to the geographic location may be generated based on the first and second location embeddings. The combined embedding may be processed using a function to determine a prediction for one or more of the geospatial measures of the geographic location of interest. | 2022-09-15 |
20220292331 | COUPLING MULTIPLE ARTIFICIALLY LEARNING UNITS WITH A PROJECTION LEVEL - Provided are methods in a system of at least a second and a third artificial intelligence unit, comprising: inputting first input values to at least one second artificial intelligence unit, and obtaining output values based on the input values from the at least one second artificial intelligence unit; at least temporarily storing situation data, the situation data comprising first input values and/or second output values of the at least one second unit; using the situation data as input values of the third artificial intelligence unit, the third artificial intelligence unit generating third output values in response to the input values; and checking whether the second output values of the at least one second unit satisfy one or more predetermined conditions based on the third output values. Also provided is a system for carrying out such a method. | 2022-09-15 |
20220292332 | SYSTEM - A system with high processing speed and low power consumption is provided. The system includes an imaging device and an arithmetic circuit. The imaging device includes an imaging portion, a first memory portion, and an arithmetic portion, and the arithmetic circuit includes a second memory portion. The imaging portion has a function of converting light reflected by an external subject into image data, and the first memory portion has a function of storing the image data and a first filter for performing first convolutional processing in a first layer of a neural network. The arithmetic portion has a function of performing the first convolutional processing using the image data and the first filter to generate first data. The second memory portion has a function of storing the first data and a plurality of filters. The arithmetic circuit has a function of generating a depth map of the image data. | 2022-09-15 |
20220292333 | PREDICTION-MODEL-BASED MAPPING AND/OR SEARCH USING A MULTI-DATA-TYPE VECTOR SPACE - In certain embodiments, content items may be obtained, where each of the content items may include multiple data types. Machine learning models may be caused to be trained based on the content items to map data in a vector space by providing at least a first portion of each of the content items as input to at least one of the machine learning models and providing at least a second portion of each of the content items as input to at least another one of the machine learning models. A search request for results may be obtained, where the search request includes search parameters. One or more locations within the vector space may be predicted (e.g., by one or more of the machine learning models) based on the search parameters. Information (indicating content items mapped to or proximate the predicted locations) may be provided as a request response. | 2022-09-15 |
20220292334 | EFFICIENT MEMORY USE OPTIMIZATION FOR NEURAL NETWORK DEPLOYMENT AND EXECUTION - Implementations disclosed describe methods and systems to perform the methods of deploying and executing machine learning models on target-specific computational platforms. Optimization techniques include but are not limited to alignment of kernel operations with hardware instructions of a target processing device, reduction of kernel dimensions near boundaries of data, efficient reuse of a small number of memory components during neural network operations, run-time quantization of data and neural network parameters, and other methods. | 2022-09-15 |
20220292335 | Reinforcement driven standard cell placement - An automatic standard cell layout generator that generates circuit layouts for an industry standard cell library on an advanced technology node leverages reinforcement learning (RL) to generate device placements in the layouts and also to fix design rule violations during routing. A genetic algorithm is utilized to generate routing candidates to which a reinforcement learning model is applied to correct the design rule constraint violations incrementally. A design rule checker provides feedback on the violations to the reinforcement learning model and the model learns how to fix the violations. A layout device placer based upon a simulated annealing method may also be utilized. | 2022-09-15 |
20220292336 | Optical Information Processing Device - An optical information processing device is a device that realizes reservoir computing using light. In the optical information processing device, a portion including a light source, an optical modulator, and an optical splitter is an input layer, a portion including optical couplers, a mode multiplexer, a mode demultiplexer, a multi-mode fiber, and an amplification and attenuator is a reservoir layer, a portion including an optical detector, a multiplier, and a summer is an output layer. The reservoir computing with light includes the input layer, the reservoir layer, and the output layer, and further includes a calculation circuit. | 2022-09-15 |
20220292337 | NEURAL NETWORK PROCESSING UNIT, NEURAL NETWORK PROCESSING METHOD AND DEVICE - A neural network processing method, a neural network processing unit (NPU) and a processing device are provided. The method includes: obtaining by a quantizing unit in the NPU float type input data, quantizing the float type input data to obtain quantized input data, and providing the quantized input data to an operation unit; performing by the operation unit of the NPU a matrix-vector operation and/or a convolution operation to the quantized input data to obtain an operation result of the quantized input data; and performing by the quantizing unit inverse quantization to the operation result output by the operation unit to obtain an inverse quantization result. | 2022-09-15 |
20220292338 | Geomechanics Informed Machine Intelligence - A computer implemented method for prediction of geomechanical performance including productivity index decline and completion integrity for a well or a hydrocarbon reservoir using a geomechanics informed machine intelligence (GIMI) algorithm. The method includes running a geomechanical reservoir simulator to generate training datasets for the hydrocarbon reservoir and incorporating physical models and identified variables into the GIMI algorithm. The method further includes training a neural network of the GIMI algorithm by using correlated training datasets that correlate to the physical models to produce a resulting prediction model and performing sensitivity analysis on the resulting prediction model. Additionally, the method includes identifying dominant variables for damage mechanisms through design of experiment statistics and performing history matching and blind test on the resulting prediction model. Lastly, the method includes updating the identified variables and models incorporated into the GIMI algorithm. | 2022-09-15 |
20220292339 | MACHINE LEARNING TECHNIQUES FOR PREDICTIVE CONFORMANCE DETERMINATION - Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing risk score generation predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform risk conformance mining predictive data analysis by utilizing machine learning frameworks that include state processing machine learning models and attribute processing machine learning models, where the machine learning frameworks may be trained as part of generative adversarial machine learning frameworks. | 2022-09-15 |
20220292340 | IDENTIFYING TRENDS USING EMBEDDING DRIFT OVER TIME - Systems, methods, and computer program products for identifying trends in behavior using embedding drift. A graph neural network may receive a network graph includes a plurality of nodes, the network graph based on a plurality of transactions for a first time interval, each transaction associated with at least one account. An embedding layer of the neural network may generate, based on the network graph, a respective embedding vector for each of the nodes. The neural network may receive a second embedding vector for each of the nodes. The neural network may determine, based on the embedding vectors and the second embedding vectors, a respective drift for each node. The neural network may determine that the drift of a first node is greater than the drift of a second node, and performing a processing operation on a first account corresponding to the first node. | 2022-09-15 |
20220292341 | LOCAL NEURAL IMPLICIT FUNCTIONS WITH MODULATED PERIODIC ACTIVATIONS - Systems and methods for signal processing are described. Embodiments receive a digital signal comprising original signal values corresponding to a discrete set of original sample locations, generate modulation parameters based on the digital signal using a modulator network, wherein each of a plurality of modulator layers of the modulator network outputs a set of the modulation parameters, and generate a predicted signal value of the digital signal at an additional location using a synthesizer network, wherein each of a plurality of synthesizer layers of the synthesizer network operates based on the set of the modulation parameters from a corresponding modulator layer of the modulator network. | 2022-09-15 |
20220292342 | Communication Efficient Federated/Distributed Learning of Neural Networks - In one set of embodiments, a client can receive from a server a copy of a neural network from a server including N layers. The client can further provide one or more data instances as input to the copy, the one or more data instances being part of a local training data set residing on the client, compute a client gradient comprising gradient values for the N layers, determine a partial client gradient comprising gradient values for a first K out of the N layers, and determine an output of a K-th layer of the copy, the output being a result of processing performed by the first K layers on the one or more data instances. The client can then transmit the partial client gradient and the output of the K-th layer to the server. | 2022-09-15 |
20220292343 | Smart Production System - Embodiments couple a corresponding IoT gateway to each IoT device, each IoT gateway monitoring for operation events of a smart contract of a distributed ledger, each IoT device and IoT gateway coupled to the distributed ledger. In response to a client initiating an operation of a first IoT device, embodiments generate a corresponding event by the smart contract and transmit an authorization request to an authorization system and in response receive an access token corresponding to the operation. Embodiments transmit the access token to one or more of the IoT gateways, each IoT gateway monitoring for the event and determining whether it corresponds to the first IoT device and then implementing the operation at the first IoT device. Embodiments determine a defect in any of the IoT devices using a trained Deep Convolutional Generative Adversarial Network (“DCGAN”) model coupled to the distributed ledger. | 2022-09-15 |
20220292344 | PROCESSING DATA IN PIXEL-TO-PIXEL NEURAL NETWORKS - Technologies are provided for processing data in neural networks. An example method can include processing, by each layer of a neural network, a row in a first stripe of a data input, the row being processed sequentially in a horizontal direction and according to a layer-by-layer sequence; after processing the row, processing, by each layer, subsequent rows in the first stripe on a row-by-row basis, each subsequent row being processed sequentially in the horizontal direction and according to the layer-by-layer sequence; generating an output stripe based on the processing of the row and subsequent rows; processing, by each layer, a second stripe of the data input, each row in the second stripe being processed in the horizontal direction and according to the layer-by-layer sequence, wherein rows in the second stripe are processed on a row-by-row basis; and generating another output stripe based on the processing of the second stripe. | 2022-09-15 |
20220292345 | DISTRIBUTIONALLY ROBUST MODEL TRAINING - Distributionally robust models are obtained by operations including training, according to a loss function, a first learning function with a training data set to produce a first model, the training data set including a plurality of samples. The operations may further include training a second learning function with the training data set to produce a second model, the second model having a higher accuracy than the first model. The operations may further include assigning an adversarial weight to each sample among the plurality of samples set based on a difference in loss between the first model and the second model. The operations may further include retraining, according to the loss function, the first learning function with the training data set to produce a distrtibutionally robust model, wherein during retraining the loss function further modifies loss associated with each sample among the plurality of samples based on the assigned adversarial weight. | 2022-09-15 |
20220292346 | SYSTEM AND METHOD FOR INTELLIGENT SERVICE INTERMEDIATION - A system and method for intelligent service intermediation comprising a service intermediation server, which stores advanced global machine and deep learning models for natural language understanding, intent analysis, and constructing a central artificial intelligence that may be used to function as one intelligent service intermediary serving many parties, each acting in one or more roles, simultaneously, and a plurality of service edge devices which store local versions of the global machine and deep learning models and which use local data to train the local model. Service intermediation server has global state information associated with all services and edge devices it connects with and may use the global state information to generate predictions and optimizations in the form of service actions in order to intermediate actions between and among services and service participants. Service actions may be executed via service edge devices by a virtual assistant representing the central artificial intelligence. | 2022-09-15 |
20220292347 | METHOD AND APPARATUS FOR PROCESSING INFORMATION - The present disclosure relates to a method and an apparatus for processing information. The method comprises: acquiring to-be-processed information, and taking the to-be-processed information as an input of a processing model acquired by training a preset model so as to acquire target information corresponding to the to-be-processed information and output by the processing model. The preset model includes a plurality of operation modules and normalization structure corresponding to each of the plurality of operation modules, the normalization structure is configured to normalize an output of the corresponding operation module, and the processing model is acquired by removing a specified number of normalization structures according to a target probability or the number of steps for training the preset model in the process of training the preset model. | 2022-09-15 |
20220292348 | DISTANCE-BASED PAIRS GENERATION FOR TRAINING METRIC NEURAL NETWORKS - Distance-based pairs generation for training metric neural networks. In an embodiment, a training batch is generated by generating a vector of distances between pairs of elements of different classes, sorting the vector, splitting the vector into blocks, assigning a coefficient to each block, and selecting pairs from the blocks based on the assigned coefficients. The training batch can then be used to train a metric neural network. | 2022-09-15 |
20220292349 | DEVICE AND COMPUTER-IMPLEMENTED METHOD FOR THE PROCESSING OF DIGITAL SENSOR DATA AND TRAINING METHOD THEREFOR - A device, computer-implemented method for the processing of digital sensor data and training methods therefor. A plurality of training tasks from a distribution of training tasks are provided, the training tasks characterizing the processing of digital sensor data. A parameter set for an architecture and for weights of an artificial neural network are determined with a first gradient-based learning algorithm and with a second gradient-based algorithm as a function of at least one first training task from the distribution of training tasks. The artificial neural network is trained with the first gradient-based learning algorithm as a function of the parameter set and as a function of a second training task. | 2022-09-15 |
20220292350 | MODEL UPDATING APPARATUS, MODEL UPDATING METHOD, AND MODEL UPDATING PROGRAM - A model updating apparatus updates a machine learning model for outputting a value of an output parameter associated with a device when a value of the input parameter associated with the device is input. The model updating apparatus has a processor, which is configured to: acquire a learning data set used for updating the machine learning model; identify a plurality of model candidates in which at least one of an algorithm and a hyperparameter is different from each other; calculate an estimate accuracy of each of the model candidates, by using the learning data set; and update the machine learning model to a model corresponding to a model candidate with the highest estimation accuracy among the model candidates. The model candidate at the time of a current update identified by the processor includes the model candidate at the time of the previous update having the highest estimation accuracy. | 2022-09-15 |
20220292351 | SYSTEMS, METHODS, AND STORAGE MEDIA FOR GENERATING SYNTHESIZED DEPTH DATA - Disclosed implementations include a depth generation method using a novel teacher-student GAN architecture (TS-GAN) to generate depth images for 2-D images, such as RGB images, where no corresponding depth information is available. An example model consists of two components, a teacher and a student. The teacher consists of a fully convolutional encoder-decoder network as a generator along with a fully convolution classification network as the discriminator. The generator takes 2-D images as inputs and aims to output the corresponding depth images. The teacher learns an initial latent mapping between 2-dimensional and co-registered depth images and the student applies the latent mapping to provide feedback to the classification network for refinement. | 2022-09-15 |
20220292352 | MACHINE-LEARNING FOR 3D SEGMENTATION - A computer-implemented method of machine-learning including obtaining a dataset of training samples. Each training sample includes a pair of 3D modeled object portions labelled with a respective value. The respective value indicates whether or not the two portions belong to a same segment of a 3D modeled object. The method further includes learning a neural network based on the dataset. The neural network takes as input two portions of a 3D modeled object representing a mechanical part and outputs a respective value. The respective value indicates an extent to which the two portions belong to a same segment of the 3D modeled object. The neural network is thereby usable for 3D segmentation. The method constitutes an improved solution for 3D segmentation. | 2022-09-15 |
20220292353 | Training Dataset, Training and Artificial Neural Network for the State Estimation of a Power Network - A method for creating for training an artificial neural network for a state estimation of a power network from a first training dataset, said dataset comprising a plurality of training pairs, each pair formed by a measurement dataset and an associated state of the power network, and the measurement dataset comprises complex apparent powers associated with the power network. The method may include: determining a first training pair, an associated measurement dataset and state with an error greater than or equal to a defined error limit; calculating a second state using a load flow calculation with a complex apparent power modified in comparison with the first dataset; calculating a second measurement dataset from the second state using a measurement model; and creating the second training dataset from the first by adding a second training pair formed from the second measurement dataset and the associated second state. | 2022-09-15 |
20220292354 | METHOD OF MANAGING DATA REPRESENTATION FOR DEEP LEARNING, METHOD OF PROCESSING DATA FOR DEEP LEARNING AND DEEP LEARNING SYSTEM PERFORMING THE SAME - A method of processing data for a deep learning system driven by a plurality of heterogeneous resources is provided. The method includes, when a first task including at least one of a plurality of operations is to be performed, receiving first path information indicating a first computing path for the first task. The first computing path includes a sequence of operations included in the first task and a driving sequence of resources for performing the operations included in the first task. The method further includes setting data representation formats of the resources for performing the operations included in the first task based on data representation information and the first path information. The data representation information indicates an optimized data representation format for each of the plurality of heterogeneous resources. | 2022-09-15 |
20220292355 | METHOD OF TRAINING A NEURAL NETWORK TO REFLECT EMOTIONAL PERCEPTION AND RELATED SYSTEM AND METHOD FOR CATEGORIZING AND FINDING ASSOCIATED CONTENT - A property vector representing extractable measurable properties, such as musical properties, of a file is mapped to semantic properties for the file. This is achieved by using artificial neural networks “ANNs” in which weights and biases are trained to align a distance dissimilarity measure in property space for pairwise comparative files back towards a corresponding semantic distance dissimilarity measure in semantic space for those same files. The result is that, once optimised, the ANNs can process any file, parsed with those properties, to identify other files sharing common traits reflective of emotional-perception, thereby rendering a more liable and true-to-life result of similarity/dissimilarity. This contrasts with simply training a neural network to consider extractable measurable properties that, in isolation, do not provide a reliable contextual relationship into the real-world. | 2022-09-15 |
20220292356 | SYSTEMS AND METHODS OF TRAINING NEURAL NETWORKS AGAINST ADVERSARIAL ATTACKS - Embodiments disclosed herein describe systems, methods, and products that generate trained neural networks that are robust against adversarial attacks. During a training phase, an illustrative computer may iteratively optimize a loss function that may include a penalty for ill-conditioned weight matrices in addition to a penalty for classification errors. Therefore, after the training phase, the trained neural network may include one or more well-conditioned weight matrices. The one or more well-conditioned weight matrices may minimize the effect of perturbations within an adversarial input thereby increasing the accuracy of classification of the adversarial input. By contrast, conventional training approaches may merely reduce the classification errors using backpropagation, and, as a result, any perturbation in an input is prone to generate a large effect on the output. | 2022-09-15 |
20220292357 | Neural Network Search Method, Apparatus, And Device - A neural network search method, apparatus, and device are provided, and relate to the field of artificial intelligence technologies, and specifically, to the field of automatic machine learning technologies. The method includes: A computing device obtains a dataset and N neural networks (S | 2022-09-15 |
20220292358 | METHOD AND SYSTEM FOR TRACKING AN OBJECT - A method of tracking an object across a stream of images comprises determining a region of interest (ROI) bounding the object in an initial frame of an image stream. A HOG map is provided for the ROI by: dividing the ROI into an array of M×N cells, each cell comprising a plurality of image pixels; and determining a HOG for each of the cells. The HOG map is stored as indicative of the features of the object. Subsequent frames are acquired from the stream of images. The frames are scanned ROI by ROI to identify a candidate ROI having a HOG map best matching the stored HOG map features. If the match meets a threshold, the stored HOG map indicative of the features of the object is updated according to the HOG map for the best matching candidate ROI. | 2022-09-15 |
20220292359 | MITIGATING OVERFITTING IN TRAINING MACHINE TRAINED NETWORKS - Some embodiments of the invention provide a novel method for training a multi-layer node network that mitigates against overfitting the adjustable parameters of the network for a particular problem. During training, the method of some embodiments adjusts the modifiable parameters of the network by iteratively identifying different interior-node, influence-attenuating masks that effectively specify different sampled networks of the multi-layer node network. An interior-node, influence-attenuating mask specifies attenuation parameters that are applied (1) to the outputs of the interior nodes of the network in some embodiments, (2) to the inputs of the interior nodes of the network in other embodiments, or (3) to the outputs and inputs of the interior nodes in still other embodiments. In each mask, the attenuation parameters can be any one of several values (e.g., three or more values) within a range of values (e.g., between 0 and 1). | 2022-09-15 |
20220292360 | PRUNING NEURAL NETWORKS - Apparatuses, systems, and techniques to remove one or more nodes of a neural network. In at least one embodiment, one or more nodes of a neural network are removed, based on, for example, whether the one or more nodes are likely to affect performance of the neural network. | 2022-09-15 |
20220292361 | METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DATA PROCESSING - Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for data processing. In a method for data processing, a first electronic device processes data based on a first data processing model to generate an initial result. A data size of the initial result is smaller than a data size of the data. The first electronic device sends the initial result to a second electronic device. The initial result is adjusted at the second electronic device and based on a second data processing model to generate an adjusted result. The second electronic device has more computing resources than the first electronic device, the second data processing model occupies more computing resources than the first data processing model, and an accuracy of the adjusted result is higher than that of the initial result. | 2022-09-15 |
20220292362 | SECRET SOFTMAX FUNCTION CALCULATION SYSTEM, SECRET SOFTMAX CALCULATION APPARATUS, SECRET SOFTMAX CALCULATION METHOD, SECRET NEURAL NETWORK CALCULATION SYSTEM, SECRET NEURAL NETWORK LEARNING SYSTEM, AND PROGRAM - Techniques for performing secure computing of softmax functions at high speed and with high accuracy are provided. A secure softmax function calculation system that calculates a share ([[softmax (u | 2022-09-15 |
20220292363 | METHOD FOR AUTOMATICALLY DETERMINING DISEASE TYPE AND ELECTRONIC APPARATUS - A disease type automatic determination method and an electronic device, wherein the method includes: the electronic device obtains comprehensive influence parameter data of several mutant genes of a tested sample on expression activity of each gene in a predetermined genome (S | 2022-09-15 |
20220292364 | DEVICE AND METHOD FOR RANDOM WALK SIMULATION - A method for simulating a random walk using spiking neuromorphic hardware is provided. The method comprises receiving, by a buffer count neuron, spiking inputs from upstream mesh nodes, wherein the inputs include information packets comprising information associated with a simulation of a random walk process. A buffer generator neuron generates spikes until the buffer count reaches a first predefined threshold, after which it sends buffer spiking outputs to a spike count neuron. The spike count neuron counts the buffer spiking outputs, and a spike generator neuron generates spikes until the spike count neuron reaches a second specified threshold. The spike generator neuron then sends counter spiking outputs to a probability neuron, which selects downstream mesh nodes to receive the counter spiking outputs, wherein the spiking outputs include updated information packets. The probability neuron then sends the spiking outputs to the selected downstream nodes. | 2022-09-15 |
20220292365 | CONVOLUTIONAL ARITHMETIC PROCESSING DEVICE AND CONVOLUTIONAL ARITHMETIC PROCESSING SYSTEM - A convolutional arithmetic processing device includes a convolutional arithmetic processor and a storage device. The convolutional arithmetic processor performs a first convolutional arithmetic process of a convolutional neural network on numerical values of a first three-dimensional array, using a type of kernel formed of numerical values of a second three-dimensional array, where a number of the type is represented by a second numerical value with a stride represented by a third numerical value in a first direction and a stride represented by a fourth numerical value in a second direction. The storage device stores at least part of the numerical values of the first three-dimensional array. | 2022-09-15 |
20220292366 | METHODS AND APPARATUS TO PERFORM LOW OVERHEAD SPARSITY ACCELERATION LOGIC FOR MULTI-PRECISION DATAFLOW IN DEEP NEURAL NETWORK ACCELERATORS - Methods, apparatus, systems, and articles of manufacture to perform low overhead sparsity acceleration logic for multi-precision dataflow in deep neural network accelerators are disclosed. An example apparatus includes a first buffer to store data corresponding to a first precision; a second buffer to store data corresponding to a second precision; and hardware control circuitry to: process a first multibit bitmap to determine an activation precision of an activation value, the first multibit bitmap including values corresponding to different precisions; process a second multibit bitmap to determine a weight precision of a weight value, the second multibit bitmap including values corresponding to different precisions; and store the activation value and the weight value in the second buffer when at least one of the activation precision or the weight precision corresponds to the second precision. | 2022-09-15 |
20220292367 | IDEATION PLATFORM DEVICE AND METHOD USING DIAGRAM - An ideation platform device and method using a diagram are disclosed. An ideation platform device using a diagram, according to one embodiment of the present invention, can comprise: a C-K canvas module for providing a C-K canvas divided into a concept space and a knowledge space and connecting a concept and knowledge to each other on the C-K canvas through a chaining process so as to help a solution search for resolving a problem; and an instance management module for storing and managing, as one instance, the C-K canvas, for which a solution search is completed, including the concept, the knowledge, and information about an interconnection relationship. | 2022-09-15 |
20220292368 | CLASSIFICATION DEVICE, LEARNING DEVICE, CLASSIFICATION METHOD, LEARNING METHOD, CLASSIFICATION PROGRAM AND LEARNING PROGRAM - A classification unit of a classification device inputs input data to a learned model for classifying data into a class, to classify a class of the input data. The learned model includes a feature value extraction model for extracting a feature value from data and a classification model for classifying a class of data based on the feature value extracted by the feature value extraction model. In the learned model, respective parameters of the feature value extraction model and the classification model are trained in advance based on a supervised data set in a first domain in such a manner that a class classification result output from the learned model and a ground truth label correspond to each other. Also, the learned model is a learned model in which the parameter of the feature value extraction model is trained in advance via adversarial learning based on the supervised data set and an unsupervised data set in a second domain in such a manner that no classification of data input for training as to whether the data is either data in the first domain or data in the second domain is performed. | 2022-09-15 |
20220292369 | METHOD, DEVICE AND MEDIUM FOR DATA PROCESSING - Embodiments of the present disclosure relate to a method, device and computer-readable storage medium for data processing. A method for data processing comprises: obtaining observed data corresponding to a plurality of factors to be analyzed; in response to one of the plurality of factors being selected as a target factor, obtaining a causal structure of the plurality of factors, the causal structure indicating causal relationships between the plurality of factors; and determining a contribution degree of a first factor of the plurality of factors to target observed data of the target factor based on the causal structure and the observed data corresponding to the plurality of factors. This solution can effectively quantify specific degrees of impact of the respective factors in the causal relationships to current observed data of the target factor, which is beneficial to analysis and policy establishment in various application scenarios. | 2022-09-15 |
20220292370 | INFORMATION PROCESSING METHOD, INFORMATION PROCESSING SYSTEM, AND INFORMATION PROCESSING DEVICE - An information processing includes: obtaining first data belonging to a first type and second data belonging to a second type different from the first type; calculating a first prediction result by inputting the first data into a first prediction model; calculating a second prediction result by inputting the first data into the second prediction model; calculating a third prediction result by inputting the second data into the second prediction model; calculating a first error between the first prediction result and the second prediction result; calculating a second error between the second prediction result and the third prediction result; and training the second prediction model by machine learning, based on the first error and the second error. | 2022-09-15 |
20220292371 | INFORMATION PROCESSING METHOD, INFORMATION PROCESSING SYSTEM, AND INFORMATION PROCESSING DEVICE - An information processing method includes: obtaining first data; calculating a first prediction result by inputting the first data into a first prediction model; calculating a second prediction result by inputting the first data into a second prediction model; calculating a degree of similarity between the first prediction result and the second prediction result; determining second data which is training data for machine learning, based on the degree of similarity; and training the second prediction model by machine learning using the second data. | 2022-09-15 |
20220292372 | METHODS AND SYSTEMS FOR PROCESSING APPROVAL REQUESTS USING PRE-AUTHORIZED APPROVAL INFORMATION IN AN APPLICATION-INDEPENDENT PROCESSING SYSTEM - Methods and systems are described herein for processing approval requests using application-independent processing system. The processing system provides a configuration file that may be used to define a process. Each application, or each process in an application, may define its own process parameters in the configuration file. The use of this architecture allows for the processing system to be used with multiple applications. When an approval request is received, the processing system obtains the configuration file of the process to which the approval request corresponds and executes the process accordingly. The processing system also facilitates automatic approval of an approval request using pre-authorized approval information that is provided by an approver independent of the approval request. If an approval request satisfies conditions defined in the pre-authorized approval information, the approval request is automatically approved without user intervention. | 2022-09-15 |
20220292373 | MANAGING USER MACHINE LEARNING (ML) MODELS - A method for receiving an end-user model access data set, deriving a plurality of patterns of actions typically performed by the end-user based on analysis of the end-user model access data set, and deriving a first model deployment protocol to automatically deploy selected ML models of the plurality of ML models for the end-user when the end-user works with ML models based on the plurality of patterns of actions. | 2022-09-15 |
20220292374 | DYNAMIC PARAMETER COLLECTION TUNING - Collected data of a first set of parameters is received via a network from one or more devices. Using machine learning, at least a portion of the collected data of the first set of parameters is analyzed to automatically identify one or more additional data parameters to be obtained to verify a detection of an incident pattern. The one or more additional data parameters are indicated to be obtained to at least a portion of the one or more devices. Collected data responsive to the indicated one or more additional data parameters is received. Based at least in part on the responsive collected data, the detection of the incident pattern is verified and a responsive action is performed. | 2022-09-15 |
20220292375 | METHOD AND SYSTEM FOR IDENTIFYING PREDICTABLE FIELDS IN AN APPLICATION FOR MACHINE LEARNING - This disclosure relates generally to identifying predictable fields in an application for machine learning (ML). With the availability of several choices for machine learning techniques, it is difficult to choose the most effective option on a specific application. In addition, the functionality/usage of fields within an application may vary across applications subject to the application's domain. Hence ML may not be efficient for all datatypes/fields. Therefore, the disclosure provides a method and system for identifying predictable fields in an application before ML technique for the predictable fields. The predictable fields are identified based on the domain of the application using a grouping technique, a pattern identification technique and optimization techniques. Further ML techniques are recommended only on identified predictable fields, thereby making the ML process more effective on the application in relevance with the application's domain. | 2022-09-15 |
20220292376 | Methods for Compressing a Neural Network - The disclosure relates to methods for compressing a neural network, wherein members of a vehicle fleet locally execute the neural network and during at least one inference phase each determine a selection of elements of the neural network that should be pruned, wherein the members of the fleet transmit the respective determined selection to a central server, wherein the central server merges the respective transmitted selections and generates a merged selection, and wherein the central server prunes the neural network on the basis of the merged selection. | 2022-09-15 |
20220292377 | COMPUTER SYSTEM AND METHOD FOR UTILIZING VARIATIONAL INFERENCE - A computing system that includes a quantum computer, wherein the computing system is configured to use variational inference methods based on input data derived from an apparatus to be controlled, and to output data for controlling the operation of the apparatus. Methods for using the computing system for controlling operation of the apparatus. The computing system uses variational inference methods configured to drawing conclusion about unobserved variable given observations of related variables, to control the apparatus. The computing system may use Bayesian networks, quantum Born machines, adversarial objectives, or kernelized Stein discrepancy, to perform variational inference. | 2022-09-15 |
20220292378 | PREPROCESSING OF TIME SERIES DATA AUTOMATICALLY FOR BETTER AI - In an approach for automatically updating the preprocessing of time series data for better AI, a processor identifies a set of characteristics from historic sensor data of a sensor, wherein the set of characteristics includes an original data granularity. A processor applies preprocessing to incoming sensor data of the sensor based on the set of characteristics. A processor, responsive to a pre-defined period of time passing, determines that a data granularity of the incoming sensor data has changed. A processor determines a new data granularity of the incoming sensor data. A processor updates the preprocessing of the incoming sensor data based on the new data granularity. | 2022-09-15 |
20220292379 | Determining a Location of Motion Detected from Wireless Signals - In a general aspect, a method for determining a location of motion detected by wireless communication devices in a wireless communication network includes obtaining motion data associated with a first time frame. The motion data includes a set of motion indicator values. The method also includes generating a first probability vector based on the set of motion indicator values and obtaining a second probability vector generated from motion data associated with a prior time frame. The method additionally includes obtaining a transition probability matrix that includes transition values and non-transition values. The method further includes determining, by operation of a data processing apparatus, a location of the motion detected from the wireless signals exchanged during the first time frame. | 2022-09-15 |
20220292380 | ADAPTIVE ANALYTICAL BEHAVIORAL AND HEALTH ASSISTANT SYSTEM AND RELATED METHOD OF USE - This present disclosure relates to systems and methods for providing an Adaptive Analytical Behavioral and Health Assistant. These systems and methods may include collecting one or more of patient behavior information, clinical information, or personal information; learning one or more patterns that cause an event based on the collected information and one or more pattern recognition algorithms; identifying one or more interventions to prevent the event from occurring or to facilitate the event based on the learned patterns; preparing a plan based on the collected information and the identified interventions; and/or presenting the plan to a user or executing the plan. | 2022-09-15 |
20220292381 | ENTANGLEMENT FORGING FOR QUANTUM SIMULATIONS - Techniques for quantum entanglement forging for quantum simulations are presented. A decomposer component can decompose a weakly entangled variational state into respective local components of the weakly entangled variational state, wherein the respective local components describe respective tensor product states. A quantum computing simulator component can perform respective quantum simulations of the respective local components of the weakly entangled variational state, and can determine respective portions of variational energy contributed by the respective tensor product states associated with the respective local components based on the respective quantum simulations of the respective local components of the weakly entangled variational state. An energy determination component can determine a variational energy associated with the weakly entangled variational state based on the respective portions of the variational energy contributed by the respective tensor product states. | 2022-09-15 |
20220292382 | RYDBERG EXCITON QUANTUM SIMULATOR - A quantum simulation method for solving a computational problem using a solid-state quantum system, the method comprising the steps of: passing a laser through a material; in the material, evolving at least some of a plurality of atoms in a first state into at least some of a plurality of atoms in a second state upon receiving energy from the laser to form at least one exciton; selecting at least one exciton site on the material wherein the at least one exciton site is separated from a neighbouring at least one exciton site by a distance less than a Rydberg blockade radius; mapping the computational problem into a problem Hamiltonian of the solid-state quantum system; measuring at least a portion of plurality of the at least one excitons to obtain a read-out of the solid-state quantum system; and determining a solution to the computational problem from the read-out. | 2022-09-15 |
20220292383 | QUANTUM INFORMATION PROCESSING DEVICE - The first layer includes a first gate electrode array disposed in the first direction to control the qubits of the qubit string, and a second gate electrode array disposed in the first direction to control the inter-qubit interaction of the interaction string. The second layer includes a third gate electrode array disposed in the second direction, and a fourth gate electrode array disposed in the second direction adjacently to the third gate electrode array. The third and the fourth gate electrode arrays control a part of the multiple qubits, and a part of the multiple inter-qubit interactions, respectively. | 2022-09-15 |
20220292384 | DEVICE WITH TWO SUPERPOSED ELECTROSTATIC CONTROL GATE LEVELS - Quantum device, including:
| 2022-09-15 |
20220292385 | FLEXIBLE INITIALIZER FOR ARBITRARILY-SIZED PARAMETRIZED QUANTUM CIRCUITS - A method and system are provided for optimizing parameters of a parametrized quantum circuit (PQC), using machine learning to train a flexible initializer for arbitrarily-sized parametrized quantum circuits. The disclosed technology may be applied to families of PQCs. Instead of using a generic or random set of initial parameters, the disclosed technology learns the structure of successful parameters from a family of related problem instances, which are then used as the machine learning training set. The method may predict optimal initializing parameters for quantum circuits having a larger number of parameters than those used in the training set. | 2022-09-15 |
20220292386 | METHOD AND SYSTEM FOR MACHINE LEARNING BASED USER EXPERIENCE EVALUATION FOR INFORMATION TECHNOLOGY SUPPORT SERVICES - Embodiments of this disclosure include a method and system for machine learning based evaluation of user experience on information technology (IT) support service. The method may include obtaining a field data of an IT support service ticket and obtaining a multi-score prediction engine. The method may further include predicting metric scores of a plurality of IT support service metrics for the support service ticket based on the field data by executing the multi-score prediction engine. The method may further include obtaining system-defined weights and user-defined weights for the plurality of service metrics and calculating a support service score for the support service ticket based on the metric scores, the system-defined weights, and the user-defined weights. The method may further include evaluating user experience based on the support service score. | 2022-09-15 |
20220292387 | BYZANTINE-ROBUST FEDERATED LEARNING - Embodiments of the present disclosure include a federated learning method by a federated learning aggregator. The method may comprise creating a log of previously provided gradients from a plurality of workers, receiving updated gradients from the plurality of workers, calculating a vulnerability weight for each layer of a global machine learning model using the updated gradients, calculating an aggregated gradient using the vulnerability weight and the updated gradients, and updating the global machine learning model using the aggregated gradient. Some embodiments may also determine whether a Byzantine attack is occurring based upon the calculated aggregated gradient. | 2022-09-15 |
20220292388 | MACHINE LEARNING MOBILE DEVICE LOCALIZATION - A machine-learning localization scheme is provided. Calibration data is received from a plurality of vehicles, the calibration data including wireless data indicative of locations of mobile devices within the plurality of vehicles, ground truth data with respect to the locations of the mobile devices, and contextual information with respect to one or more of operating system versions of the mobile devices or battery levels of the mobile devices. A machine-learning model is trained using the wireless data and the contextual information as inputs and the ground truth data as output. Responsive to an error rate for the machine-learning model being within an error target, the machine-learning model is provided to the plurality of vehicles. | 2022-09-15 |
20220292389 | BIOINFORMATICS PROCESSING ORCHESTRATION - Computer software that performs the following operations: (i) identifying a bioinformatics dataset and instructions for processing the bioinformatics dataset, the instructions identifying a sequence of bioinformatics processing tools including at least a first bioinformatics processing tool followed by a second bioinformatics processing tool; (ii) instructing the first bioinformatics processing tool to process the bioinformatics dataset in accordance with the instructions; (iii) analyzing an output of the first bioinformatics processing tool, utilizing a machine learning based decision model, to determine a modification to the sequence of bioinformatics processing tools; and (iv) instructing a third bioinformatics processing tool to process at least a first portion of the bioinformatics dataset in accordance with the determined modification. | 2022-09-15 |
20220292390 | UNIFORM ARTIFICIAL INTELLIGENCE MODEL CONVERSION - Aspects of the invention include converting an artificial intelligence (AI) model generated in a first framework to a uniform exchange formatted model by engaging a master table to retrieve instructions for converting from the AI model to the uniform exchange formatted model in accordance with the first framework. The uniform exchange formatted model in compiled by engaging the master table to retrieve instructions for compiling the uniform exchange formatted model in accordance with the first framework. Data is received as an input to the compiled uniform exchange formatted model and an output is generated by engaging the master table to retrieve instructions for generating the output in accordance with the first framework. | 2022-09-15 |
20220292391 | INTERPRETABLE MODEL CHANGES - In a method for interpreting output of a machine learning model, a processor receives a first interpretable rule set. A processor may also receive a second interpretable rule set generated from a dataset and model-predicted labels classifying the dataset. A processor may also generate a difference metric and mapping between the first interpretable rule set and the second interpretable rule set. | 2022-09-15 |
20220292392 | SCHEDULED FEDERATED LEARNING FOR ENHANCED SEARCH - An indication of availability over time and resource usage is maintained for each computing device of a plurality of computing devices. An optimal combination of a subset of the plurality of computing devices is determined for each round of one or more rounds of training based on the availability over time and the resource usage for each computing device. A global model is generated utilizing the one or more optimal combinations of the plurality of computing devices and a query is performed utilizing the global model. | 2022-09-15 |
20220292393 | UTILIZING MACHINE LEARNING MODELS TO GENERATE INITIATIVE PLANS - A device may receive and process client data, with a first machine learning model, to determine current state data identifying a current state of a client. The device may process the current state data and prior client data, with a second machine learning model, to determine a problem statement for the client and future state data of the client. The device may utilize the second machine learning model to identify initiatives for the client, and costs of the initiatives, based on the problem statement, the current state data, and the future state data, and to assign benefits and priorities to the initiatives. The device may process the initiatives, the benefits and priorities of the initiatives, and the costs of the initiatives, with the second machine learning model, to generate an initiative plan for solving a problem of the problem statement, and may perform actions based on the initiative plan. | 2022-09-15 |
20220292394 | MULTI-SCALE DEEP SUPERVISION BASED REVERSE ATTENTION MODEL - A multi-scale deep supervision based reverse attention model is provided and includes an input end, a multi-scale feature learning module, an attention mechanism module, a reverse attention mechanism module, a deep supervision module, multiple loss functions, multiple average pool layers, multiple linear layers and multiple branches. The reverse attention mechanism module as provided can alleviate the problem of feature information loss caused by attention mechanisms, and part of the modules can be discarded in the testing phase, thereby improving the testing efficiency. | 2022-09-15 |
20220292395 | METHOD AND SYSTEM FOR CREATING A PREDICTIVE MODEL FOR TARGETING WEB-PAGE TO A SURFER - Systems and methods for determining predictive model types are provided. A method may include generating a predictive model for a web page of a web site, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein each object is associated with a predictive model. The method may include determining one or more predictive model types that are associated with the predictive model, determining one or more performance indicators that correspond to each determined predictive model type, wherein performance indicators represent one or more benefits to a website, selecting a predictive model type of the predictive model out of the one or more predictive model types, wherein the predictive model type is selected based on a performance indicator corresponding to the selected predictive model type, and determining a configuration of the web page using the selected predictive model type of the predictive model. | 2022-09-15 |
20220292396 | METHOD AND SYSTEM FOR GENERATING TRAINING DATA FOR A MACHINE-LEARNING ALGORITHM - A method and a system for generating training data for an MLA are provided. The method comprises: retrieving assessor data associated with a plurality of assessors, the assessor data including data indicative of a plurality of results responsive to a given digital task having been submitted to the plurality of assessors; based on the plurality of results, determining at least one set of assessors in the plurality of assessors, such that a consistency metric amongst results provided by the at least one set of assessors for the given digital task is maximized, transmitting a subsequent digital task to respective electronic devices associated with the at least one set of assessors; and generating the training data for the computer-executable MLA including data generated in response to respective ones of the at least one set of assessors completing the subsequent digital task. | 2022-09-15 |
20220292397 | RECOGNITION SYSTEM, MODEL PROCESSING APPARATUS, MODEL PROCESSING METHOD, AND RECORDING MEDIUM - The server device receives a model information from a plurality of terminal devices, and generates an integrated model by integrating the model information received from the plurality of terminal devices. The server device generates an updated model by learning a model defined by the model information received from the terminal device of update-target using the integrated model. Then, the server device transmits the model information of the updated model to the terminal device. Thereafter, the terminal device executes recognition processing using updated model. | 2022-09-15 |
20220292398 | METHODS, APPARATUS AND MACHINE-READABLE MEDIA RELATING TO MACHINE-LEARNING IN A COMMUNICATION NETWORK - A method performed by a first network entity in a communications network includes training a model to obtain a local model update including an update to values of one or more parameters of the model, in which training the model includes inputting training data into a machine learning algorithm. The method further includes applying a serialisation function to the local model update to construct a serial representation of the local model update, thereby removing information indicative of a structure of the model, and transmitting the serial representation of the local model update to an aggregator entity in the communications network. | 2022-09-15 |
20220292399 | PROCESSING OF REDUCTION AND BROADCAST OPERATIONS ON LARGE DATASETS WITH MUTLI-DIMENSIONAL HARDWARE ACCELERATORS - Methods, systems, and apparatus, including instructions encoded on storage media, for performing reduction of gradient vectors and similarly structured data that are generated in parallel, for example, on nodes organized in a mesh or torus topology defined by connections in at least two dimension between the nodes. The methods provide parallel computation and communication between nodes in the topology. | 2022-09-15 |
20220292400 | SYSTEMS AND METHODS OF PROCESSING DIVERSE DATA SETS WITH A NEURAL NETWORK TO GENERATE SYNTHESIZED DATA SETS FOR PREDICTING A TARGET METRIC - A computer system stores data sets, a target metric, and a parameter that indicates a desired number of synthesized data sets, and a neural network. The neural network includes a summing node and multiple processing nodes. One or more hardware processors is configured to perform operations where each processing node of a neural network weights input data set values, determines gating operations to select processing operations, and generates a node output by applying the gating operations to weighted input data set values. Weighted node outputs from the processing nodes produce a value for the target parameter. The neural network is trained until the neural network converges. One or more nodes is selected, and for each selected node, a subset of the input data sets and a subset of the gating operations are selected. The selected input data set values are processed with the selected processing nodes using the selected subset of gating operations to produce synthesized data sets. | 2022-09-15 |
20220292401 | IDENTIFYING OPTIMAL WEIGHTS TO IMPROVE PREDICTION ACCURACY IN MACHINE LEARNING TECHNIQUES - A computer-implemented method, system and computer program product for improving prediction accuracy in machine learning techniques. A teacher model is constructed, where the teacher model generates a weight for each data case. The current student model is then trained using training data and the weights generated by the teacher model. After training the current student model, the current student model generates state features, which are used by the teacher model to generate new weights. A candidate student model is then trained using training data and these new weights. A reward is generated by comparing the current student model with the candidate student model using training and testing data, which is used to update the teacher model if a stopping rule has not been satisfied. Upon a stopping rule being satisfied, the weights generated by the teacher model are deemed to be the “optimal” weights which are returned to the user. | 2022-09-15 |
20220292402 | ROAD CONDITION DEEP LEARNING MODEL - The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wetness and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules, external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth. The model can be applied in a variety of ways to enhance autonomous vehicle operation, for instance by altering current driving actions, modifying planned routes or trajectories, activating on-board cleaning systems, etc. | 2022-09-15 |
20220292403 | ENTITY RESOLUTION INCORPORATING DATA FROM VARIOUS DATA SOURCES WHICH USES TOKENS AND NORMALIZES RECORDS - A pair of records is tokenized to form a normalized representation of an entity represented by each record. The tokens are correlated to a machine learning system by determining whether a learned resolution already exists for the two entities. If not, the normalized records are compared to generate a comparison measure to determine whether the records match. The normalized records can also be used to perform a web search and web search results can be normalized and used as additional records for matching. When a match is found, the records are updated to indicate that they match, and the match is provided to the machine learning system to update the learned resolutions. | 2022-09-15 |
20220292404 | BLACK-BOX OPTIMIZATION USING NEURAL NETWORKS - Methods and systems for determining an optimized setting for one or more process parameters of a machine learning training process. One of the methods includes processing a current network input using a recurrent neural network in accordance with first values of the network parameters to obtain a current network output, obtaining a measure of the performance of the machine learning training process with an updated setting defined by the current network output, and generating a new network input that comprises (i) the updated setting defined by the current network output and (ii) the measure of the performance of the training process with the updated setting defined by the current network output. | 2022-09-15 |
20220292405 | METHODS, SYSTEMS, AND FRAMEWORKS FOR DATA ANALYTICS USING MACHINE LEARNING - Some embodiments relate to methods, systems, and frameworks for data analytics using machine learning, such as methods and systems for preprocessing of biomedical data, using machine learning, for input to a predictive model. The method may include receiving data from a data source, using at least one machine learning (ML) algorithm from a plurality of ML algorithms to obtain at least one combination of preprocessing steps, and computing an accuracy score for each of the at least one combination based on accuracy of prediction of the predictive model. The method may further include using at least one ML algorithm to optimize the feature selection of the predictive model, combining a plurality of datasets into a single dataset, and using a parallel computing network to provide a framework for executing such predictive model. | 2022-09-15 |
20220292406 | ANALYZING AND ENABLING SHIFTS IN GROUP DYNAMICS - Analyzing and enabling shifts in group dynamics by receiving data regarding interactions of a plurality of participants, determining an interaction context according to the data, determining interaction dynamics according to the interaction context using a first machine learning model, determining an interaction trend between a first participant and a second participant, according to the interaction dynamics, using a second machine learning model, detecting a bias between the first participant and the second participant according to the interaction trend, generating a remediation action to shift the interaction dynamics and providing the remediation action to at least one participant. | 2022-09-15 |
20220292407 | SYSTEM AND METHOD FOR MACHINE LEARNING-BASED DELIVERY TAGGING - A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: receiving historical delivery records over a predetermined time period from partners associated with items offered to subregions through an online platform; generating nodes for combinations each comprising a respective one of the partners, a respective one of the items offered by the partners, and a respective one of the subregions; generating, using a machine learning model, a respective classification for each respective node on whether to tag the each respective node as deliverable in a predetermined time window; and automatically tagging a portion of the nodes as deliverable in the predetermined time window in the online platform. Other embodiments are disclosed. | 2022-09-15 |
20220292408 | ENSEMBLE FORECAST STORM DAMAGE RESPONSE SYSTEM FOR CRITICAL INFRASTRUCTURE - A storm damage response system includes a storm ensemble database that stores ensemble forecast models associated with potential storm paths of a storm across a geographic area and an inventory database that stores inventory data associated with location and characteristics of power-providing equipment in the geographic area. A storm damage model algorithm generates a storm response plan comprising an operational procedure for repairing or maintaining power transmission and distribution electric systems to mitigate storm damage impact based on generating a probabilistic model for each of the ensemble forecast models based on the inventory data and calculating a statistical impact value associated with the probabilistic model based on an aggregate of iterative probabilistic simulations for the respective ensemble forecast model. The storm response plan can be generated based on the relative statistical impact value of the probabilistic model of each of the ensemble forecast models. | 2022-09-15 |
20220292409 | RESERVATION ACCEPTING SYSTEM AND RESERVATION ACCEPTING METHOD - A system includes one or more processors configured to: acquire request information including a desired date and time, a point of departure, and a destination, from a user placing a reservation for use of a moving body; derive a reservation result indicating whether the reservation is completed for the request information; derive, based on the request information, the reservation result indicating completion, suspension, or failure for the request information once or a plurality of times; derive, at a first time of deriving the reservation result, one of reservation results including at least the reservation result indicating the completion and the reservation result indicating the suspension for the request information; derive the reservation result indicating the completion or the failure by a predetermined deadline for the request information for which the reservation result indicates the suspension; and notify the user of the reservation result that has been derived. | 2022-09-15 |
20220292410 | Venue Seat Assignment Based Upon Hearing Profiles - The concepts and technologies disclosed herein are directed to venue seat assignment based upon hearing profiles. According to one aspect of the concepts and technologies disclosed herein, a device can include a processor and a memory. The device can receive a request to upload a hearing profile, and can upload the hearing profile to a seat assignment system. The device can receive, from the seat assignment system, a customized seating chart based, at least in part, upon the hearing profile. The customized seating chart can include a visual representation of at least a portion of seating in a venue. The device can select, from the customized seating chart, a seat from the portion of the seating in the venue. This selection can be made automatically or based upon user input. | 2022-09-15 |
20220292411 | METHOD AND SYSTEM FOR PROVIDING EQUIPMENT RENTAL SERVICE USING BIOMETRIC ID CARD - A portable device for biometric authentication includes a sensor configured to acquire a biometric pattern from a user who attempts to access the portable device, a biometric information storage section configured to store biometric information of a registered subscriber who has been registered to be authorized to use the portable device, a public key certificate storage section configured to store a public key certificate, and a biometric information matching section operatively coupled to the sensor, the biometric information storage section, and the public key certificate storage section. In particular, biometric information that matches the registered subscriber's biometric information is encoded and inserted into the public key certificate, and the biometric information matching section authenticates in response to determining that the user's biometric pattern acquired by the sensor matches the registered subscriber's encoded biometric information inserted in the public key certificate. | 2022-09-15 |
20220292412 | INTEGRATED END-TO-END TRAVEL INSTRUMENT (TI) DEVICE GENERATION SYSTEM AND INTEGRATED TRAVEL INSTRUMENT DEVICE - The system includes a webserver ( | 2022-09-15 |
20220292413 | BIKE SHARING REBALANCING OPTIMIZATION METHOD BASED ON ADAPTIVE NEIGHBORHOOD SEARCH ALGORITHM - The present disclosure provides a bike sharing rebalancing optimization method based on an adaptive neighborhood search algorithm. First, based on a difference between locations of shared bikes and users' travel demands in temporal and spatial distribution, a bike sharing rebalancing model is constructed. Next, an adaptive neighborhood search algorithm is constructed by using an adaptive operator selection mechanism and based on the fusion of a large neighborhood search algorithm and a neighborhood search algorithm. Then, seven types of perturbation operators, six types of repair operators, and eight types of neighborhood search operators are designed based on features of rebalancing time, pickup-and-delivery actions, and a capacity limit of a vehicle. Finally, a termination condition is constructed such that the algorithm terminates at an appropriate time and an optimization solution for a rebalancing route is provided. | 2022-09-15 |
20220292414 | DYNAMIC INVITATION TRANSMISSION AND PRESENTATION MODE DETERMINATION FOR A NETWORK-BASED SERVICE - A network system can receive a first request for a transport service and a second request for the transport service. The system can identify, from a plurality of service providers, a first set of service providers for the first request, and a second set of service providers for the second request. Based on a first set of predictive parameters for the first set of service providers, the system implements a multi-invite mode by transmitting a first invitation data set to service the first request to a plurality of provider devices of the first set of service providers. Based on a second set of predictive parameters for the second set of service providers, the system implements an exclusive-invite mode by transmitting a second invitation data set to a provider device of a selected service provider of the second set of service providers. | 2022-09-15 |
20220292415 | Automated Playbook Generation - An example embodiment includes determining, from a target set of incident reports, a set of putative steps; determining a set of playbook steps by identifying a set of clusters within the set of putative steps, wherein each playbook step of the set of playbook steps corresponds to a respective cluster within the identified set of clusters, and wherein each cluster within the identified set of clusters contains at least one putative step of the set of putative steps; determining a sequence for the set of playbook steps based on an ordering of the putative steps within the target set of incident reports and the correspondences between the putative steps and the identified set of clusters; and displaying, on a user interface, an indication of the set of playbook steps according to the determined sequence for the set of playbook steps. | 2022-09-15 |
20220292416 | SYSTEM AND METHOD FOR OPTIMIZING DESIGN, WORKFLOWS, PERFORMANCE, AND CONFIGURATIONS BASED ON DESIGN ELEMENTS - Methods, systems, and computer-readable media are disclosed herein for an application that optimizes the design element and workflow configuration of a target computer program. Generally, the application automatically displays preview images of graphical user interface in response to a user-input answer in a questionnaire, where the preview image accounts for design element and/or workflow impacts to the graphical user interface of the target computer program. The application also predicts performance indicators for target computer program that account for design element and/or workflow steps directly or indirectly selected through the user-input answers. | 2022-09-15 |
20220292417 | USING WEIGHTED PEER GROUPS TO SELECTIVELY TRIGGER A SECURITY ALERT - Techniques are described herein that are capable of using weighted peer groups to selectively trigger a security alert. A determination is made that an entity performs an operation. The entity has peers that are categorized among peer groups. For each peer group, an extent to which the peers in the peer group perform the operation is determined. Weights are assigned to the respective peer groups. For each peer group, the extent to which the peers in the peer group perform the operation and the weight that is assigned to the peer group are combined to provide a respective weighted group value. A risk score, which is based at least in part on the weighted group values of the peer groups, is assigned to the operation. The security alert regarding the operation is selectively triggered based at least in part on the risk score. | 2022-09-15 |
20220292418 | OPERATION RISK EVALUATION SYSTEM, MODEL CREATION APPARATUS, OPERATION RISK EVALUATION METHOD, AND RECORDING MEDIUM - An operation risk evaluation system includes: a model storage unit configured to store a surrogate model that is a learned model of a relationship between an input that is a first feature amount of the operation and an output that is a second feature amount of the operation calculated by a predetermined simulation of one of the steps, the learned model being configured to surrogate for the predetermined simulation in which an output corresponding to an input having the same value has indeterminity different for each input; a prediction unit configured to predict a plurality of the second feature amounts having indeterminity corresponding to the first feature amount having the same value in the step; and a risk evaluation unit configured to evaluate a risk of the operation in the step based on the plurality of second feature amounts having indeterminity predicted by the prediction unit. | 2022-09-15 |
20220292419 | COMPUTER-BASED INFORMATION MANAGEMENT SYSTEM CONFIGURED FOR AUTOMATED AND DYNAMIC ACCOUNT ANALYSIS AND METHODS THEREOF - Systems and methods of the present disclosure enable user-level activity recordation using population level activity data by receiving operator data including a record of activities performed by users on an operator system. Each entry in the record of activities is parsed to form structured activity entries representing each activity executed on the operator system. Each entry in the record of activities is matched to an individual account in an account database based on an individual identifier of each entry and an account individual identifier identifying the individual account. A statistical metric representing the activity history of the individual account is produced based on each entry matched to the individual account, and an activity history dashboard is displayed on an operator computing device to depicts the statistical metric for the individual account. | 2022-09-15 |
20220292420 | Survey and Result Analysis Cycle Using Experience and Operations Data - Embodiments implement a survey and result analysis cycle combining user experience and software operations data. A central survey engine receives from a survey designer, a configuration package specifying one or more of the following survey attributes: survey questions; operational data relevant to the survey for collection; rules; a target user group; and a survey triggering event. In response, the survey engine collects applicable operational data from software being evaluated, determines the actual users to be targeted by the survey, and promulgates the survey. Feedback from the survey is received and stored as a package including both the experience data (e.g., survey questions/responses) and operational data (e.g., specific operational data collected from the software that is relevant to the survey questions). This package is sent to a vendor to assist in analyzing the experience of the user of the software, and also to potentially devise valuable questions for a follow-up survey. | 2022-09-15 |
20220292421 | METHODS AND APPARATUS FOR ARTIFICIAL INTELLIGENCE CONVERSION OF CHANGE ORDERS INTO AN ACTIONABLE INTERFACE - Artificial Intelligence systems receive two dimensional representations (e.g. physical or electronic documents) that are processed to mimic the perception, learning, problem-solving, and decision-making formerly performed by human workers. AI analysis is repeated for multiple two dimensional representations over time, each two dimensional reference including a change to a design of a building to be constructed. The AI processes denote and track changes made in the sequence of two dimensional references and extrapolate changes to materials and labor that relate to the changes in design of the building to be constructed. | 2022-09-15 |
20220292422 | ADVANCED SEARCH ENGINE FOR FEDERAL SPEND AND USER INTERFACE FOR THE SAME - Systems and methods applicable, for instance, to federal spend search engines and user interfaces. Data operations can transform and enhance raw federal spend data. Further, users can be presented with information that can be understood and consumed at a glance. | 2022-09-15 |
20220292423 | MULTI-SERVICE BUSINESS PLATFORM SYSTEM HAVING REPORTING SYSTEMS AND METHODS - The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems. A system and method for providing reporting-related services to client entities. These services may determine a set of reportable properties based on a type of chart and at least one data source selected by a user. A report plan may be executed on a knowledge graph to obtain a reporting dataset. A custom chart may be generated based on the type of chart selected, the reporting dataset, and a set of reporting parameters. | 2022-09-15 |