53rd week of 2020 patent applcation highlights part 56 |
Patent application number | Title | Published |
20200410339 | LEARNING SYSTEM, REHABILITATION SUPPORT SYSTEM, METHOD, PROGRAM, AND TRAINED MODEL - A learning unit of a learning system generates the following learning model. That is, this learning model is a model that inputs rehabilitation data for each predetermined period and predicts a change in index data, the rehabilitation data being data about rehabilitation performed by a trainee using a rehabilitation support system, the index data indicating at least one of a symptom, a physical ability, and a degree of recovery of the trainee. This rehabilitation data includes at least the index data and training data of the trainee, the training data being acquired during the rehabilitation in the rehabilitation support system. Further, the learning unit generates the learning model by using, as teacher data, data obtained in a period until the index data reaches a predetermined target level. | 2020-12-31 |
20200410340 | INFORMATION PROCESSING DEVICE AND INFORMATION PROCESSING METHOD - An information processing device includes: a processor configured to: calculate a combination of t and q minimizing a computation time when q computation cores compute convolution between first matrices and second matrices of t-row t-column with Winograd algorithm in parallel, where a total number of elements of the first and second matrices does not exceed a number of sets of data that can be stored in each of q storage areas of a register, and the q computation cores correspond to the q storage areas; and output a program for causing a computing machine including the q computation cores and the register to execute a process including: storing the first and second matrices in each of the q storage areas with a calculated combination of t and q, and computing convolution between the first matrix and the second matrix with the Winograd algorithm by each of the q computation cores. | 2020-12-31 |
20200410341 | LEARNING SYSTEM, REHABILITATION SUPPORT SYSTEM, METHOD, PROGRAM, AND TRAINED MODEL - A learning unit of a learning system generates the following learning model. That is, this learning model is a model that inputs, for each predetermined period, rehabilitation data about rehabilitation performed by a trainee using a rehabilitation support system, and predicts a change in a setting parameter. The setting parameter is a setting parameter in the rehabilitation support system that is used when the trainee performs the rehabilitation. The rehabilitation data includes at least index data, trainee data, and training data, the index data indicating at least one of a symptom, a physical ability, and a degree of recovery of the trainee, the trainee data indicating a feature of the trainee, the training data including the setting parameter. Further, the learning unit generates a learning model by using, as teacher data, data obtained in a period until the index data reaches a predetermined target level. | 2020-12-31 |
20200410342 | METHOD FOR TRAINING AN ARTIFICIAL NEURAL NETWORK, ARTIFICIAL NEURAL NETWORK, USE OF AN ARTIFICIAL NEURAL NETWORK, AND CORRESPONDING COMPUTER PROGRAM, MACHINE-READABLE MEMORY MEDIUM, AND CORRESPONDING APPARATUS - A method for training an artificial neural network, in particular a Bayesian neural network, by way of training data sets, having a step of adapting the parameters of the artificial neural network depending on a loss function, the loss function encompassing a first term that represents an estimate of a lower bound of the distances between the classifications of the training data sets by the artificial neural network and the expected classifications of the training data sets. The loss function further encompasses a second term that is configured in such a way that differences in the aleatoric uncertainty in the training data sets over different samples of the artificial neural network are regulated. | 2020-12-31 |
20200410343 | QUANTUM COMPUTATION THROUGH REINFORCEMENT LEARNING - Methods, systems, and apparatus for designing a quantum control trajectory for implementing a quantum gate using quantum hardware. In one aspect, a method includes the actions of representing the quantum gate as a sequence of control actions and applying a reinforcement learning model to iteratively adjust each control action in the sequence of control actions to determine a quantum control trajectory that implements the quantum gate and reduces leakage, infidelity and total runtime of the quantum gate to improve its robustness of performance against control noise during the iterative adjustments. | 2020-12-31 |
20200410344 | FAST DECODING IN SEQUENCE MODELS USING DISCRETE LATENT VARIABLES - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes receiving the input sequence; processing the input sequence using a latent prediction model configured to autoregressively predict a sequence of discrete latent variables that is shorter than the output sequence and that encodes the output sequence, wherein each discrete latent variable in the sequence is selected from a discrete set of latent variables; and processing the input sequence and the predicted sequence of discrete latent variables using a parallel decoder model configured to generate the outputs in the output sequence in parallel from the input sequence and the predicted sequence of discrete latent variables. | 2020-12-31 |
20200410345 | LEARNING QUALITY ESTIMATION DEVICE, METHOD, AND PROGRAM - To provide a learning quality estimation device, a learning quality estimation method, and a program that can remove wrong data in learning data used for machine learning such as natural language processing. A learning quality estimation device | 2020-12-31 |
20200410346 | SYSTEMS AND METHODS FOR USING AND TRAINING A NEURAL NETWORK - There is provided a controller for control of a processor based system, comprising: a hardware processor(s) executing a code for: during an inference process of a neural network: feeding into the neural network (NN) input signals from sensors monitoring the processor based system, wherein the feeding triggers propagation of a forward dataflow in a forward direction from input to output and a non-forward dataflow in a non-forward direction from output to input, wherein the non-forward dataflow occurs at least one of before and simultaneously with the forward dataflow, wherein the forward dataflow and the non-forward dataflow establish candidate communication channels each mapping the input signals to candidate outputs, wherein a single communication channel is selected from the candidate communication channels, and outputting a single response mapped to the input signals by the single communication channel, the single response denoting instructions for control of the processor based system. | 2020-12-31 |
20200410347 | METHOD AND DEVICE FOR ASCERTAINING A NETWORK CONFIGURATION OF A NEURAL NETWORK - A method for ascertaining a suitable network configuration for a neural network for a predefined application that is determined in the form of training data. The method includes: a) starting from an instantaneous network configuration, generating multiple network configurations which differ in a portion of the instantaneous network configuration by applying approximate network morphisms; b) ascertaining affected network portions of the network configurations; c) multiphase training of each of the network configurations to be evaluated, under predetermined training conditions, in a first phase, in each case network parameters of a portion that is not changed by applying the particular approximate network morphism remaining unconsidered during the training, and all network parameters being trained in at least one further phase, d) determining a resulting prediction error for each of the network configurations to be evaluated; e) selecting the suitable network configuration as a function of the determined prediction errors. | 2020-12-31 |
20200410348 | LEARNING DEVICE, LEARNING METHOD, AND LEARNING PROGRAM - A learning device ( | 2020-12-31 |
20200410349 | ARCHITECTURES, SYSTEMS AND METHODS HAVING SEGREGATED SECURE AND PUBLIC FUNCTIONS - A system is provided for control of an entertainment state system having segregated secure functions and public functions for use by one or more users of the system. First, a public interface portal receives instructions regarding operation of the entertainment state system from the one or more users. The interface portal includes a first interface, a processor, a graphical user interface (GUI) coupled to the processor, a control unit in operative communication with the processor and graphical user interface, and a second interface providing an application program interface (API). Secondly, a secure entity unit is provided, the secure entity unit including a receive interface, the receive interface adapted to receive a call from the application program interface (API) of the interface portal, a send interface, the send interface adapted to provide a response to the interface portal interface, a game engine, and a financial engine. | 2020-12-31 |
20200410350 | ANOMALY DETECTION - According to an exemplary embodiment of the present disclosure, disclosed is a computer program stored in a computer readable storage medium. When the computer program is executed in one or more processors, the computer program performs the following method for anomaly detection of data using a network function, and the method includes: generating an anomaly detection model including a plurality of anomaly detection sub models including a trained network function using a plurality of training data sub sets included in the training data set; calculating input data using at least one of the plurality of generated anomaly detection sub models; and determining whether there is an anomaly in the input data based on output data for input data of at least one of the plurality of generated anomaly detection sub models and the input data. | 2020-12-31 |
20200410351 | CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an actor neural network used to select actions to be performed by an agent interacting with an environment. One of the methods includes obtaining a minibatch of experience tuples; and updating current values of the parameters of the actor neural network, comprising: for each experience tuple in the minibatch: processing the training observation and the training action in the experience tuple using a critic neural network to determine a neural network output for the experience tuple, and determining a target neural network output for the experience tuple; updating current values of the parameters of the critic neural network using errors between the target neural network outputs and the neural network outputs; and updating the current values of the parameters of the actor neural network using the critic neural network. | 2020-12-31 |
20200410352 | SYSTEM AND METHODS FOR PROCESSING SPATIAL DATA - A system for processing spatial data may be designed to receive neural network outputs corresponding to a first spatial data set, and translate the neural network outputs corresponding to the first spatial data set based on the motion between a second spatial data set and the first spatial data set. The system may perform zero-gap run length encoding on the neural network outputs to store the neural network outputs in memory. The system may also perform on-the-fly skip zero decoding and bilinear interpolation to translate the neural network outputs. | 2020-12-31 |
20200410353 | HARMONIC DENSELY CONNECTING METHOD OF BLOCK OF CONVOLUTIONAL NEURAL NETWORK MODEL AND SYSTEM THEREOF - A harmonic densely connecting method includes an input step, a plurality of layer operation steps and an output step. The input step is for storing an original input tensor of the block into a memory. Each of the layer operation steps includes a layer-input tensor concatenating step and a convolution operation step. The layer-input tensor concatenating step is for selecting at least one layer-input element tensor of a layer-input set from the memory according to an input connection rule. When a number of the at least one layer-input element tensor is greater than 1, concatenating all of the layer-input element tensors and producing a layer-input tensor. The convolution operation step is for calculating a convolution operation to produce at least one result tensor and then storing the at least one result tensor into the memory. The output step is for outputting a block output. | 2020-12-31 |
20200410354 | NEURAL NETWORK LAYER-BY-LAYER DEBUGGING - Techniques are disclosed for debugging a neural network execution on a target processor. A reference processor may generate a plurality of first reference tensors for the neural network. The neural network may be repeatedly reduced to produce a plurality of lengths. For each of the lengths, a compiler converts the neural network into first machine instructions, the target processor executes the first machine instructions to generate a first device tensor, and the debugger program determines whether the first device tensor matches a first reference tensor. A shortest length is identified for which the first device tensor does not match the first reference tensor. Tensor output is enabled for a lower-level intermediate representation of the shortest neural network, and the neural network is converted into second machine instructions, which are executed by the target processor to generate a second device tensor. | 2020-12-31 |
20200410355 | EXPLAINABLE MACHINE LEARNING BASED ON HETEROGENEOUS DATA - Methods and systems for explainable machine learning are described. In an example, a processor can receive a data set from a plurality of data sources corresponding to a plurality of domains. The processor can train a machine learning model to learn a plurality of vectors that indicate impact of the plurality of domains on a plurality of assets. The machine learning model can be operable to generate forecasts relating to performance metrics of the plurality of assets based on the plurality of vectors. In some examples, the machine learning model can be a neural attention network with shared hidden layers. | 2020-12-31 |
20200410356 | METHOD FOR OPTIMIZING A DATA MODEL AND DEVICE USING THE SAME - A method for optimizing a data model is used in a device. The device acquires data information and selecting at least two data models according to the data information, and utilizes the data information to train the at least two data models. The device acquires each accuracy of the at least two data models, determines a target data model which has greatest accuracy between the at least two data models, and optimizes the target data model. | 2020-12-31 |
20200410357 | AUTOMATIC THRESHOLDS FOR NEURAL NETWORK PRUNING AND RETRAINING - An embodiment includes a method, comprising: pruning a layer of a neural network having multiple layers using a threshold; and repeating the pruning of the layer of the neural network using a different threshold until a pruning error of the pruned layer reaches a pruning error allowance. | 2020-12-31 |
20200410358 | EFFICIENT ARTIFICIAL INTELLIGENCE ACCELERATOR - Artificial intelligence workloads can take advantage of low-precision hardware to reduce their hardware overhead compared to high-precision systems. Stochastic rounding is used to enable low bit-width operations. Disclosed are systems and methods for artificial intelligence accelerators that provide efficient rounding for low bit-width operations and other processing tasks by reusing and sharing random numbers among operations and arithmetic logic units. | 2020-12-31 |
20200410359 | COMPUTING DEVICE AND PARAMETER SYNCHRONIZATION METHOD IMPLEMENTED BY COMPUTING DEVICE - A parameter synchronization method is implemented in a computing device. The parameter synchronization method includes importing a deep learning training task of a preset model into a server communicatively coupled to the computing device, recording a preset number of iterative processes during the deep learning training, dividing each iterative process into a number of phases according to time, determining whether a time ratio of an H2D phase, a D2H phase, and a CPU phase in each iterative process is greater than a preset value, and confirming the server to use a copy mode for performing parameter synchronization when the time ratio of the H2D, D2H, and CPU phases is determined to be greater than the preset value. | 2020-12-31 |
20200410360 | INFORMATION PROCESSING METHOD AND INFORMATION PROCESSING APPARATUS - According to one embodiment, an information processing method includes performing, in an intermediate layer of a deep neural network, a forward propagation using a first parameter and based on a first input value represented by a first bit number; performing quantization to produce a second input value represented by a second bit number smaller than the first bit number, and storing the produced second input value in the memory; calculating a second parameter based on a result of an operation using the second input value stored in the memory and a value obtained by the forward propagation, the second parameter being an update of the first parameter and for use in the learning process; and determining a condition for the quantization based on a gradient difference obtained in said calculating the second parameter. | 2020-12-31 |
20200410361 | INFORMATION PROCESSING APPARATUS, CONTROL METHOD, AND NON-TRANSITORY STORAGE MEDIUM - An information processing apparatus ( | 2020-12-31 |
20200410362 | OPTIMIZING NEURAL NETWORKS FOR RISK ASSESSMENT - Certain embodiments involve generating or optimizing a neural network for risk assessment. The neural network can be generated using a relationship between various predictor variables and an outcome (e.g., a condition's presence or absence). The neural network can be used to determine a relationship between each of the predictor variables and a risk indicator. The neural network can be optimized by iteratively adjusting the neural network such that a monotonic relationship exists between each of the predictor variables and the risk indicator. The optimized neural network can be used both for accurately determining risk indicators using predictor variables and determining adverse action codes for the predictor variables, which indicate an effect or an amount of impact that a given predictor variable has on the risk indicator. The neural network can be used to generate adverse action codes upon which consumer behavior can be modified to improve the risk indicator score. | 2020-12-31 |
20200410363 | ABNORMALITY DETECTION SYSTEM AND ABNORMALITY DETECTION PROGRAM - An abnormality detection system has a detection target waveform generation unit and a detection target waveform determination/abnormality detection unit. The detection target waveform includes a target waveform detection algorithm learning the detection target waveform and generates an expected detection target waveform by executing the target waveform detection algorithm for an input waveform. The detection target waveform determination/abnormality detection unit compares the expected detection target waveform with the input waveform to determine that the input waveform corresponds to the detection target waveform. | 2020-12-31 |
20200410364 | METHOD FOR ESTIMATING A GLOBAL UNCERTAINTY OF A NEURAL NETWORK - A method for estimating a global uncertainty of output data of a computer implemented main neural network. The method includes determining a first measure quantifying to which extent the current input data of the main neural network is following the same distribution as the data, which was used for training the main neural network; generating a second measure quantifying the main neural network's certainty in its own prediction based on the input data; ascertaining a third measure, based on an estimation of class-discriminative features in the input data and a comparison of these features with a class activation probability distribution, especially wherein the class activation probability distribution was created based on estimated class-discriminative features during the training of the main neural network; and determining the global uncertainty based on at least two measures of the first, second and third measure. | 2020-12-31 |
20200410365 | UNSUPERVISED NEURAL NETWORK TRAINING USING LEARNED OPTIMIZERS - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a base neural network that generates numeric representations of network inputs. | 2020-12-31 |
20200410366 | AUTOMATIC DETERMINATION OF THE RUN PARAMETERS FOR A SOFTWARE APPLICATION ON AN INFORMATION PROCESSING PLATFORM BY GENETIC ALGORITHM AND ENHANCED NOISE MANAGEMENT - A method for optimizing the run parameters of a software application on an information processing platform, consisting of iteratively optimizing said parameters on each execution of said application, wherein, for each execution of said application ( | 2020-12-31 |
20200410367 | Scalable Predictive Analytic System - A system for validating models for predicting a client behavior event includes a development module and a validation module. The development module is configured to receive a use case corresponding to the client behavior event and select a subset of variables correlated to the client behavior event. The validation module is configured to select a first model from models that predict client behavior event using the selected subset of variables. The development module selects the first model based on a predicted lift of the first model. The validation module applies the first model to client data acquired subsequent to the selection of the first model. The validation module compares the predicted lift of the first model to an actual lift of the first model as applied to the client data. The validation module selects one of the first model and a different model in response to the comparison. | 2020-12-31 |
20200410368 | EXTENDED RULE GENERATION - Provided is a method, computer program product, and system for generating extended rules from common rules for vehicles. A processor may receive real-time data from one or more internet of things (IoT) devices that are communicatively coupled to a vehicle. The processor may analyze the real-time data by applying one or more event detection rules. The processor may determine that the one or more event detection rules have not been met. The processor may extract contextual data from the real-time data. The processor may correlate the contextual data with the one or more event detection rules. The processor may generate, in response to the correlating, one or more extended rules incorporating the contextual data. The processor may apply the one or more generated extended rules to the real-time data. | 2020-12-31 |
20200410369 | DATA-DRIVEN CROSS FEATURE GENERATION - Techniques for generating cross features using a data driven approach are provided. Multiple possible splits of a numerical feature are identified. For each split, the numerical feature is transformed into a second feature based on the split, a cross feature is generated based on the second feature and a third (e.g., categorial) feature that is different than the first feature and the second feature, a predictive power of the cross feature is estimated, and the predictive power is added to a set of estimated predictive powers. After each split is considered, a cross feature that is associated with the highest estimated predictive power in the set of estimated predictive powers is selected. That first cross feature corresponds to a particular split from the multiple possible splits. The numerical feature is split based on the particular split to generate a bucketized version of the numerical feature. | 2020-12-31 |
20200410370 | TRAINING OF PHOTONIC RESERVOIR COMPUTING SYSTEMS - A photonics reservoir computing system is described. The system is configured for propagating at least one optical signal so as to create resulting radiation signals in the output channels. The photonics reservoir computing system further comprises weighting elements for weighting signals from the output channels, and at least one optical detector for optically detecting signals from the output channels. The system is adapted for estimating signals from the output channels through an output of the optical detector. | 2020-12-31 |
20200410371 | DATA ANALYSIS METHOD AND DEVICE - A data analysis method and device are provided. The method includes: acquiring historical behavior data of a user, wherein the historical behavior data includes information on a historical time period and a historical behavior in the historical time period; selecting first historical behavior data meeting a first preset condition from the historical behavior data of the user; and determining a habit of the user based on the first historical behavior data meeting the first preset condition. Historical behavior data may be analyzed to obtain a habit of a user, thereby improving an intelligence degree of a system. | 2020-12-31 |
20200410372 | OPTIMIZATION APPARATUS, CONTROL METHOD FOR OPTIMIZATION APPARATUS, AND RECORDING MEDIUM - An optimization apparatus includes a memory; and a processor coupled to the memory and the processor configured to: compute a local solution for a combinatorial optimization problem based on a first evaluation function representing the combinatorial optimization problem, select a state variable group targeted by partial problems from the plurality of state variables based on a first state variable whose value at the local solution is a predetermined value among the plurality of state variables included in the first evaluation function, a weight coefficient representing a magnitude of an interaction between the plurality of state variables held in a storage unit, and input selection region information, search a ground state for a second evaluation function representing the partial problems for the selected state variable group, and generate a whole solution by updating the local solution based on the partial solutions acquired by the ground state search. | 2020-12-31 |
20200410373 | PREDICTIVE ANALYTIC METHOD FOR PATTERN AND TREND RECOGNITION IN DATASETS - A computer-implemented method for predicting output values in a multidimensional dataset comprising the steps of arranging a multidimensional dataset in a hierarchical order to a two-dimensional order; computing randomness of different permutations of variables; reordering the hierarchical order based on the randomness; computing contribution of each variable to an output; interpolating or extrapolating contribution values of each variable via mapping technique; and determining a predictive value for any given input by summing up the impact of each variable determined previously. | 2020-12-31 |
20200410374 | SYSTEM AND METHOD FOR FLEET MANAGEMENT OF PORTABLE OXYGEN CONCENTRATORS - A system and method for prediction of the time to service components for a fleet of portable oxygen concentrators (POCs) is disclosed. Each of the POCs include a transmitter to transmit operational data. A network interface is configured to receive operational data from the POCs. A user database contains profiles of users associated with respective POCs. An analysis engine updates the profile of a user associated with a POC in the user database based on received operational data from the POC. The analysis engine determines a similar profile in the user database to the updated profile. The analysis engine predicts a service date for the component of the POC based on the similar profile and the updated profile. | 2020-12-31 |
20200410375 | OPTIMIZING AND PREDICTING AVAILABILITY OF RESOURCES IN A SHARED VEHICLE ENVIRONMENT - An intelligent bicycle sharing system, or other vehicle sharing system, is able to provide helpful bicycle availability predictions based on historical data, including various utilization statistics. Historical data can be collected over time as users use the bicycle sharing system. For example, the historical data may include the number of available bicycles at various locations and times, as well as contextual data associated with the locations and times. Contextual data may include data regarding the weather, local events, season, day of the week or year, news events, among other environmental factors that may or may not influence bicycle utilization. In some embodiments, a model, such as a machine learning model (e.g., neural network) may be trained using the historical data as training data such that the model can predict bicycle availability for a certain future time and location. | 2020-12-31 |
20200410376 | PREDICTION METHOD, TRAINING METHOD, APPARATUS, AND COMPUTER STORAGE MEDIUM - A method of modeling a numerical relationship between a user quantity indicator and a resource usage indicator includes performing first regression on a first dataset that describes a numerical relationship between a feature of the user quantity indicator and a feature of a service usage indicator, to obtain a first prediction model. The method further includes performing second regression on a second dataset that describes a numerical relationship between the feature of the service usage indicator and a feature of the resource usage indicator, to obtain a second prediction model. | 2020-12-31 |
20200410377 | SYSTEMS AND METHODS FOR SYNERGISTIC SHARING OF ARCHITECTURAL COMPONENTS OF INTELLIGENT AGENTS - Systems and methods are described for sharing components among intelligent agents, such as robot agents that perform tasks autonomously. The intelligent agents may include functional components implemented in a middleware layer that provides an interface among the functional components. The functional components may include components for sensory information processing, managing goals and tasks, planning tasks, knowledge bases, and effector information processing. The middleware layer of the intelligent agents may include a component sharing layer. The component sharing layer may search for and identify components running on agents of an agent group that satisfy one or more constraints specified by a requesting component. The component sharing layer may establish a connection between the requesting component and the identified component. The requesting component may utilize services of the identified component to complete a goal or task. | 2020-12-31 |
20200410378 | Telephone Call Assessment Using Artificial Intelligence - Techniques are described relating to automatically classifying telephone calls into a particular category using machine learning and artificial intelligence technology. As one example, calls to a customer service phone number can be classified as related to prohibited activity, or as legitimate. In particular, a number of different telephony variables as well as additional variables can be used to make such a classification, after training an appropriate machine learning model. The training process may use an externally provided call classification score that is provide by an outside entity as an input, and can be calibrated so that the output score of the trained classifier provides a score that corresponds to a real-world percentage chance of an unclassified call falling into a particular category. Thus, a classifier score of “95” can indicate that a call is in fact believed to be 95% likely to correspond to prohibited activity, for example. | 2020-12-31 |
20200410379 | COMPUTATIONAL CREATIVITY BASED ON A TUNABLE CREATIVITY CONTROL FUNCTION OF A MODEL - Systems, computer-implemented methods, and computer program products that can facilitate computational creativity are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a learner component that learns mappings of data features from a feature space to a creativity attribute of a model to define a creativity control function of the model. The computer executable components can further comprise a generator component that employs the model to generate a creative data sample based on the creativity control function. | 2020-12-31 |
20200410380 | UNSUPERVISED CLUSTERING IN QUANTUM FEATURE SPACES USING QUANTUM SIMILARITY MATRICES - A method of performing unsupervised clustering of data points includes determining a number of qubits to include in a quantum processor based on feature dimensions of each data point. The method includes, for each pair of data points, executing a quantum circuit on a quantum processor having the determined number of qubits. The quantum circuit includes a feature map template circuit parameterized with a first plurality of rotations, a backward feature map template circuit parameterized with a second plurality of rotations, and a measurement circuit that outputs a similarity measure. The method includes creating a similarity matrix based on the similarity measure for each pair of data points, and inputting the similarity matrix to a classical clustering algorithm to cluster the data points. The feature map template circuit and the backward feature map template circuit each use quantum properties of superposition and entanglement of the qubits of the quantum processor. | 2020-12-31 |
20200410381 | DETERMINING A DISTANCE - Methods and apparatus are disclosed, including an example of a method of determining a distance between a first point and a second point. The method includes manipulating quantum states of at least first and second qubits of a quantum computing device based on a test vector representing the first point and a training vector representing the second point, performing quantum interference between the test vector and the training vector, performing a measurement on one or more of the qubits to determine the distance, and determining the distance from the measurement. | 2020-12-31 |
20200410382 | TARGETING MANY-BODY EIGENSTATES ON A QUANTUM COMPUTER - Methods, systems and apparatus for targeting many-body states on a quantum computer. In one aspect, a method includes an adaptive phase shift method that includes preparing the quantum system in an initial state, wherein the initial state has non-zero overlap with the target eigenstate; preparing an ancilla qubit in a zero computational basis state; and iteratively applying a quantum eigenstate locking circuit to the quantum system and ancilla qubit until the state of the quantum system approximates the target eigenstate, wherein the quantum eigenstate locking circuit comprises a phase gate that, at each n-th iteration, is updated using a current average energy estimate of the quantum system. | 2020-12-31 |
20200410383 | NUCLEAR SPEIN QUANTUM PROCESSING ELEMENT AND METHOD OF OPERATION THEREOF - The present disclosure is directed a quantum processing element comprising: a semiconductor and a dielectric material forming an interface with the semiconductor; a dopant atom with nuclear spin of quantum number larger than ½ embedded in the semiconductor at a distance from the interface, at least one conductive electrode disposed in a manner such that there is at least a portion of dielectric material between the at least one conductive electrode and the dopant atom. The disclosure is also directed to a method of operating the quantum processing element comprising the steps of: applying a magnetic field to the dopant atom to separate the energies of the spin states associated with the nucleus of the dopant atom; applying a voltage to the at least one conductive electrode to generate an electric field gradient at a nucleus of the dopant atom; and encoding quantum information in the nuclear spin of the nucleus via the applied voltage. | 2020-12-31 |
20200410384 | HYBRID QUANTUM-CLASSICAL GENERATIVE MODELS FOR LEARNING DATA DISTRIBUTIONS - Hybrid quantum-classical generative models for learning data distributions are provided. In various embodiments, methods of and computer program products for operating a Helmholtz machine are provided. In various embodiments, methods of and computer program products for operating a generative adversarial network are provided. In various embodiments, methods of and computer program products for variational autoencoding are provided. | 2020-12-31 |
20200410385 | LEARNING SYSTEM, REHABILITATION SUPPORT SYSTEM, METHOD, PROGRAM, AND TRAINED MODEL - A learning unit of a learning system generates a learning model, the learning model being configured to input rehabilitation data about rehabilitation and predict feedback control to be performed, the rehabilitation being performed by a trainee using a rehabilitation support system. The rehabilitation support system performs the feedback control based on motivation information of the trainee. The rehabilitation data includes at least training data including the motivation information of the trainee and feedback information indicating the feedback control. The learning unit generates the learning model by using, as teacher data, the rehabilitation data that is obtained when the motivation information is one that causes such a change that the motivation of the trainee is improved. | 2020-12-31 |
20200410386 | AUTOMATIC AND CONTINUOUS MONITORING AND REMEDIATION OF API INTEGRATIONS - Monitoring and automatically remediating issues that arise at run-time during integrations between Application Program Interfaces (APIs) of two or more endpoint products over an integration framework. The monitoring is facilitated by inserting specialized modules into the integration framework that detects changes in the outputs of the integrated endpoint products and attempts to remedy them by automatically adjusting the output in-transit towards a destination endpoint. The specialized modules can be enhanced by machine learning algorithms trained on previously successful remedies. Remedies may be directed towards schema variations and performance drifts, among others. | 2020-12-31 |
20200410387 | Minimizing Risk Using Machine Learning Techniques - Embodiments relate to an intelligent computer platform to utilize machine learning techniques to for task planning optimization. Tasks and task characteristics are collected and tracked over defined temporal segments. Data points and corresponding measurements of the collected and tracked tasks and task characteristics are temporally analyzed. Statistically significant data associated with the tracked tasks are identified in response to the identification of a statistical deviation in the analyzed data points. A path of the tracked tasks is modified to create an optimal delivery path in view of the identified statistical deviation. One or more encoded actions are executed in compliance with the modified path. | 2020-12-31 |
20200410388 | MODEL TRAINING USING A TEACHER-STUDENT LEARNING PARADIGM - A method and a system for model training are provided. The method can include training a first classifier, a second classifier, and a third classifier with subsets of a labeled dataset. The method can also include predicting a pseudo labeled dataset from an unlabeled dataset using the first classifier, the second classifier, and the third classifier. The method further includes assigning a role to the first classifier, to the second classifier, and to the third classifier. The method can further include selecting a teaching sample dataset from the pseudo labeled dataset based on the role assigned to the third classifier, wherein the third classifier is assigned a role of a student. The method can also include retraining the third classifier with the teaching sample dataset in conjunction with a subset of the labeled dataset. | 2020-12-31 |
20200410389 | SELF-OPTIMIZING MULTI-CORE INTEGRATED CIRCUIT - A self-optimizing System-on-Chip (SOC) includes multiple cores, multiple hardware accelerators, multiple memories and an interconnect framework. The SOC also includes a machine learning (ML) module that uses data flow information to build a ML network dynamically and configures all the various hardware blocks autonomously, to achieve predetermined application performance targets. The SOC is able to recover from hangs caused when testing various configuration settings. The SOC also avoids configuration settings that cause severe drops in performance. | 2020-12-31 |
20200410390 | MACHINE LEARNING RETRAINING - The behavior of a machine learning model and the training dataset used to train the model are monitored to determine when the accuracy of the model's predictions indicate that the model should be retrained. The retraining is determined from one or more precision metrics and a coverage metric that are generated during operation of the model. A precision metric measures the ability of the model to make predictions that are accepted by an inference system and the coverage metric measures the ability of the model to make predictions given a set of input features. In addition, changes made to the training dataset are analyzed and used as an indication of when the model should be retrained. | 2020-12-31 |
20200410391 | PERSONAL HELPER BOT SYSTEM - BOT enhanced systems can engage users in a conversational manner with natural language commands to coordinate activity of a team of autonomous helper BOTs. Among a variety of other tasks, ROTS collaborate to create the users' schedules, maintain their to-do lists, obtain personally interesting information, provide personalized services and searches, perform web site transactions, use all types of web apps, complete tasks and/or synthesize useful products for their owners. The disclosed systems and processes leverage digital library architectures to house elements that they intelligently serve and user-directed search paradigms to refine and customize the search function according to the searcher's preferences, style and demographic information. | 2020-12-31 |
20200410392 | TASK-AWARE COMMAND RECOMMENDATION AND PROACTIVE HELP - A task-aware command recommendation system and related techniques are described herein. The task-aware command recommendation system can provide a user of a software application (e.g., an analytics application or other software application) with guidance by predicting commands that can be executed to accomplish a given task. For example, an ongoing task being performed by a user can be determined based on commands that have been performed by the user up to a current point in time. Information about the task can be incorporated into one or more command recommendation models, which can determine one or more commands to recommend to the user for performing the task. In some cases, the task-aware command recommendation system can include a help prediction model that can anticipate when the user is having difficulties completing a task, and can provide help for the user to continue performing the task. | 2020-12-31 |
20200410393 | System and Method for Examining Data from a Source - A system and method are provided for examining data from a source. The method is executed by a device having a processor and includes receiving a set of historical data and a set of current data to be examined, from the source. The method also includes generating multiple statistical models based on the historical data and a forecast for each model. The method also includes selecting one of the multiple statistical models based on at least one criterion, and generating a new forecast using the selected model. The method also includes comparing the set of current data against the new forecast to identify any data points in the set of current data with unexpected values. The method also includes outputting a result of the comparison, the result comprising any data points with unexpected values. | 2020-12-31 |
20200410394 | PREDICTING FUTURE ACTIONS DURING VISUAL DATA CLEANING - System and methods are described for visual data cleaning in a cloud computing environment. A method includes receiving a request for transformation of data values in cells of a selected column of a data set stored in a memory, applying the transformation on the selected column, if the selected column exists in a hierarchical relationship graph, determining zero or more columns of the data set affected by the transformation on the selected column according to the hierarchical relationship graph, and if there are one or more affected columns, predicting expected values for cells in the one or more affected columns according to a knowledge base. | 2020-12-31 |
20200410395 | SYSTEM AND METHOD FOR COMPLEX TASK MACHINE LEARNING - An electronic device for complex task machine learning includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to receive an unknown command for performing a task and generate a prompt regarding the unknown command. The at least one processor is also configured to receive one or more instructions in response to the prompt, where each of the one or more instructions provides information on performing at least a portion of the task. The at least one processor is further configured to determine at least one action for each one of the one or more instructions. In addition, the at least one processor is configured to create a complex action for performing the task based on the at least one action for each one of the one or more instructions. | 2020-12-31 |
20200410396 | IMPLICIT BRIDGING OF MACHINE LEARNING TASKS - Methods, systems, and apparatus, including computer programs encoded on computer storage media for performing machine learning tasks. One method includes receiving (i) a model input, and (ii) data identifying a first machine learning task to be performed on the model input to generate a first type of model output for the model input; augmenting the model input with an identifier for the first machine learning task to generate an augmented model input; and processing the augmented model input using a machine learning model, wherein the machine learning model has been trained on training data to perform a plurality of machine learning tasks including the first machine learning task, and wherein the machine learning model has been configured through training to process the augmented model input to generate a machine learning model output of the first type for the model input. | 2020-12-31 |
20200410397 | SEARCH SYSTEM, SEARCH METHOD, AND PROGRAM - A search system comprising: a learner that calculates a feature quantity of information that is input and outputs a classification result of the information based on the feature quantity; and at least one processor configured to: store at least one of a feature quantity or a classification result of information to be searched, which has been input in the learner, in a database corresponding to a classification of the information to be searched among a plurality of databases prepared for respective classifications; input input information in the learner; and search for information to be searched that is similar to the input information in at least one of the feature quantity or the classification result based on a database corresponding to a classification result of the input information that is output from the learner among the plurality of databases prepared for respective classifications. | 2020-12-31 |
20200410398 | Methods and Devices for Chunk Based IoT Service Inspection - A method for chunk based lot service inspection is provided. The method is implemented by a network device in a communication network. Data of IoT service may be received. The data may include a plurality of packets from a network node. The plurality of packets may be shaped into one or more chunks based on packet header information of each packet. Each chunk may include one or more packets. One or more characteristic parameters for each of the one or more chunks may be generated based on one or more properties of the one or more packets in said chunk. A cluster label may be identified for each chunk based on shaping the plurality of packets into one or more the one or more characteristic parameters of said chunk. | 2020-12-31 |
20200410399 | METHOD AND SYSTEM FOR DETERMINING POLICIES, RULES, AND AGENT CHARACTERISTICS, FOR AUTOMATING AGENTS, AND PROTECTION - A method of automatically configuring an action determination model includes determining an environment model, determining an action determination model that indicates an action option, determining whether the action determination model indicates a next action option, and if so, determining an action based on the action determination model, simulating execution of the action across the environment model, obtaining a simulated result, adjusting the action determination model. Then, until environment or an agent reach an end state, the following are repeated: determining whether the action determination model indicates the next action option, and if so, determining the action based on the action determination model, simulating the execution of the action across the environment model, obtaining the simulated result, and adjusting the action determination model. | 2020-12-31 |
20200410400 | EXTRACTING FACTS FROM UNSTRUCTURED DATA - Methods, systems, and apparatus, including computer programs encoded on computer storage media, to present a video. One of the methods includes obtaining one or more unstructured documents. The method includes obtaining, by a computer system, a data model, the data model identifying a type of fact that can be determined from the one or more unstructured documents. The method includes determining, by the computer system, a channel to extract facts from the document based on the type of fact. The method includes distributing, by the computer system, the one or more unstructured documents to the channel. The method includes extracting, by the channel, facts from the one or more unstructured documents. The method also includes storing the facts in a data model. | 2020-12-31 |
20200410401 | System and Method for Searching and Matching Content Over Social Networks to an Individual - The present invention is directed at a system and method for searching and matching content over social networks relevant to a specific individual. In an aspect, the individual relevant content search system provides search results and information that is relevant to the individual's perspective. | 2020-12-31 |
20200410402 | BIONIC COMPUTING SYSTEM AND CLOUD SYSTEM THEREOF - The bionic computing system includes a perception subsystem, an attention subsystem, and a temporal-spatial awareness subsystem. The perception subsystem has several perceptual devices for detecting objects from sequences of sensory data and generating an object record for each object. The attention subsystem adjusts the object records by re-identifying the tracking identities across sensory devices, generates several object associations, generate several location associations, and generates several motion implications. The temporal-spatial awareness subsystem organizes and retains the object records in a working memory space. The perception subsystem identifies several basic events from each sensory datum of the same perceptual device, the attention subsystem identifies an episodic event by determining that a portion of the basic events from the same sensory device within a temporal window conform to a pattern, and the temporal-spatial awareness subsystem identifies a complex event according the detected episodic event and the object records in the working memory space. | 2020-12-31 |
20200410403 | SYSTEM AND METHOD FOR DETECTING DATA DRIFT - Data drift or dataset shift is detected between training dataset and test dataset by training a scoring function using a pooled dataset, the pooled dataset including a union of the training dataset and the test dataset; obtaining an outlier score for each instance in the training dataset and the test dataset based at least in part on the scoring function; assigning a weight to each outlier score based at least in part on training contamination rates; determining a test statistic based at least in part on the outlier scores and the weights; determining a null distribution of no dataset shift for the test statistic; determining a threshold in the null distribution; and when the test statistic is greater than or equal to the threshold, identifying dataset shift between the training dataset and the test dataset. | 2020-12-31 |
20200410404 | SYSTEMS, CIRCUITS AND COMPUTER PROGRAM PRODUCTS PROVIDING A FRAMEWORK FOR SECURED COLLABORATIVE TRAINING USING HYPER-DIMENSIONAL VECTOR BASED DATA ENCODING/DECODING AND RELATED METHODS - A computing system can include a plurality of clients located outside a cloud-based computing environment, where each of the clients may be configured to encode respective original data with a respective unique secret key to generate data hypervectors that encode the original data. A collaborative machine learning system can operate in the cloud-based computing environment and can be operatively coupled to the plurality of clients, where the collaborative machine learning system can be configured to operate on the data hypervectors that encode the original data to train a machine learning model operated by the collaborative machine learning system or to generate an inference from the machine learning model. | 2020-12-31 |
20200410405 | ORGANIZED CARPOOLS PROVIDED BY RIDESHARE SERVICE - The present technology pertains to providing an improved user experience for user accounts taking group rideshare rides. In some aspects of the present technology, a rideshare service can use ride experience data relating to a group rideshare ride experience by a user account to determine that a user associated with the user account might have reason to be dissatisfied with the group rideshare ride, and can compensate the user account. In some aspects of the present technology, a rideshare service can proactively suggest a group rideshare itinerary to a user account when the group itinerary matches a previous itinerary arranged by the user account, and the suggested group rideshare itinerary can be offered at a preferred charge. In some aspects of the present technology, a rideshare service can permit a user account to create an organized carpool ride with other invited user accounts. | 2020-12-31 |
20200410406 | AUTONOMOUS VEHICLE RIDER DROP-OFF TO DESTINATION EXPERIENCE - An example method for assisting autonomous vehicle (AV) riders reach their destination after drop-off can include receiving, by an autonomous vehicle (AV), a ride service request from a user, the ride service request specifying a pick-up location, a pick-up time, and a destination of a trip associated with the ride service request; navigating the AV to the pick-up location; providing the user a recommendation for where to sit within the AV, the recommendation being based on at least one of the destination of the trip, a drop-off location associated with the trip, environment conditions associated with the drop-off location, and an estimated egress location; receiving sensor data from one or more sensors associated with the AV; determining, based on the sensor data and the drop-off location, a pull-over location for dropping off the user; and navigating the AV to the pull-over location. | 2020-12-31 |
20200410407 | RESOURCE RESERVATION SYSTEM, REGISTRATION TERMINAL, AND SETTING METHOD - A resource reservation system includes an information processing apparatus and an information processing terminal. The information processing apparatus includes a memory, and a processor coupled to the memory and configured to transmit reservation information of a resource to the information processing terminal, store a communication setting of the information processing terminal associated with the resource, generate a first web page that displays the communication setting of the information processing terminal and that is displayed by a registration terminal, and receive a change in the communication setting of the information processing terminal. The change in the communication setting is made on the first web page displayed by the registration terminal. The second processor switches whether to transmit the reservation information in accordance with the communication setting of the information processing terminal, in response to a request from the information processing terminal. | 2020-12-31 |
20200410408 | WHEELCHAIR BOARDING INFORMATION TRANSMISSION SYSTEM AND WHEELCHAIR BOARDING INFORMATION DISPLAY SYSTEM - An autonomous driving vehicle is a bus provided with wheelchair passenger spaces therein. The autonomous driving vehicle detects whether or not a wheelchair is on board in the wheelchair passenger spaces, and transmits the obtained wheelchair boarding information to a control center. The control center transmits the received wheelchair boarding information to a bus stop sign and causes the bus stop sign to display the information on a display of the bus stop sign. The wheelchair boarding information can also be displayed on a touch panel display of a smartphone. | 2020-12-31 |
20200410409 | AUTOMATED SCHEDULING OF EVENTS WITH MULTIPLE PERFORMERS - Systems and methods are provided for assigned a plurality of timeslots for an event to a plurality of performers. A plurality of ticket requests from a plurality of computing devices are tracked at a server. Each ticket request includes an indicator representing at least one of the plurality of performers. A score is assigned to each of the plurality of performers based on the indicators associated with each performer across the plurality of ticket requests. A plurality of timeslots for the event are assigned to the plurality of performers according to the determined score for each of the plurality of performers. | 2020-12-31 |
20200410410 | Computer-Implemented Method, System, and Computer Program Product for Automated Forecasting - A computer-implemented method for automated forecasting of cash flow includes: monitoring, while a plurality of first transactions are being processed in a payment network, payable transaction data associated with the plurality of first transactions, the plurality of first transactions initiated with at least one account issued to a merchant; monitoring, while a plurality of second transactions are being processed in a payment network, receivable transaction data associated with the plurality of second transactions, the plurality of second transactions between the merchant and a plurality of users; determining, based on the payable transaction data and the receivable transaction data, a plurality of seasonal variables; and generating a cash flow forecast associated with the merchant, the cash flow forecast generated based on the plurality of seasonal variables. A system and computer program product for automated forecasting of cash flow are also disclosed. | 2020-12-31 |
20200410411 | Auditing Compliance with an Optimized Travel Route for Product Procurement - A method for auditing implementation of a travel route for procuring items includes receiving a list of items to be procured. An optimized travel route is created for procuring the items. The optimized travel route is sent to a mobile electronic computing device. Information is received from the mobile electronic computing device regarding purchases made for the items. The information received is used to determine compliance with the optimized travel route. When a determination of non-compliance with the optimized travel route is made, information indicating non-compliance with the optimized travel route is sent to the mobile electronic computing device for display on a display screen of the mobile electronic computing device. | 2020-12-31 |
20200410412 | SYSTEMS AND METHODS FOR A SMART VIRTUAL QUEUE - A system includes a virtual queue controller configured to receive an indication of a reduced capacity event from an amusement park attraction, determine a reduction in capacity of the attraction, identify each guest having a return time in a virtual queue of the attraction that is affected by the reduced capacity event, remove guests having affected return times from the virtual queue, and generate a reaccommodation time slot for the guests removed from the virtual queue, select two or more updated return times within the reaccommodation time slot for each of the guests removed from the virtual queue, provide a notification to each guest removed from the virtual queue requesting guest input to select a single updated return time from the two or more updated return times, and return each guest to the virtual queue upon receiving a corresponding selection of the single updated return time. | 2020-12-31 |
20200410413 | DATA PROCESSING APPARATUS, DATA PROCESSING METHOD, AND RECORDING MEDIUM - A data processing apparatus includes: a first storage part that stores an analysis result that specifies each region when a feature space is divided such that a distribution of each data group associated with a predetermined step of a manufacturing process in the space is classified according to an effect calculated for each data group in the predetermined step; a second storage part that stores models each of which outputs the effect corresponding to each region, in association with each region, when the data groups classified into each region of the feature space are inputted; and an execution part for performing a simulation processing by using, among the models, a model stored in association with one region when a new data group associated with the predetermined step is acquired and when the one region into which the acquired new data group is classified is determined based on the analysis result. | 2020-12-31 |
20200410414 | SYSTEM AND METHOD FOR WORKFLOW MANAGEMENT - A workflow management system for managing the storage, retrieval, and transport of items in a warehouse includes a voice-directed mobile terminal. The system also includes a server computer in communication with the voice-directed mobile terminal. The server computer includes a tasking module for transmitting task data to the voice-directed mobile terminal. The server computer also includes a workflow-analysis module for generating, based at least in part upon an analysis of workflow dialog between the voice-directed mobile terminal and the user, performance data relating to the performance of tasks associated with the storage, retrieval, and/or transport of the items. | 2020-12-31 |
20200410415 | COMPUTER-BASED SYSTEMS FOR RISK-BASED PROGRAMMING - Techniques are described for correlating risk information and providing notifications to different devices based on the correlated risk information. An example computing device includes a memory, one or more processors, and a communication unit. The memory stores input data and a risk data model with respect to the input data. The processor(s) evaluate the input data for first risk information associated with a first remote device, and correlate the first risk information to second risk information to form a risk correlation, based on one or more characteristics of the input data. The processor(s) also determine that the second risk information is associated with a second remote device that is different from the first remote device, and generate a notification that includes information linking the second risk information to the input data. The communication unit transmits the notification to the second remote device. | 2020-12-31 |
20200410416 | METHOD FOR SCHEDULING OR CONTROLLING THE MOVEMENTS OF A PLURALITY OF VEHICLES OVER A NETWORK OF ROUTES - A method for scheduling or controlling the movements of a plurality of vehicles over a network of routes is disclosed. The nodes and edges of the network of routes are formed by route elements. The control and management of route elements by a central authority is complex and susceptible to disturbances. The method enables a simplification of the technical infrastructure that is to be maintained within the network and improves its availability and thus also the resistance of the network to technical disruptions. This is achieved in that any vehicle as an entity represented in a distributed ledger system enters into transaction agreements with the route elements, likewise represented as entities in this distributed ledger system, wherein each transaction agreement of a vehicle with a route element includes at least one time specification, which defines the period of time for which the route element is occupied by the vehicle. | 2020-12-31 |
20200410417 | AUTOMATED DISPATCH OPTIMIZATION - Systems and methods are disclosed for automated dispatch optimization. In one implementation, a first request is received. One or more constraints associated with the first request are identified. Based on the identified constraints, a dispatch window is computed with respect to the first request. A second request is received. Based on the second request, a dispatch of the first request is adjusted within the dispatch window. | 2020-12-31 |
20200410418 | ANALYZING CLOUD BACKUP SERVICE OPTIONS USING HISTORICAL DATA PROTECTION ACTIVITIES - Historical activity data about backups and restorations are retrieved. Description files corresponding to cloud storage providers are received. Each description file includes a name of a cloud storage provider, a catalog listing cloud service options offered by the cloud storage provider, and pricing and descriptive information for the options. The historical activity data is mapped to the cloud service options. A set of cost figures is generated based on the mapping. Each cost figure represents a cost that would have been charged to a user, based on the historical activity data, by the cloud storage provider for storing the backups and accessing the backups for the restorations. The cloud storage providers are rated using the cost figures and the rated cloud storage providers are displayed in a user interface to allow the user to select a particular cloud storage provider to which the backups are to be migrated. | 2020-12-31 |
20200410419 | SERVICE AREA MAPS FOR AUTONOMOUS VEHICLES - Aspects of the technology relate to providing service area maps for an autonomous vehicle transportation service having a fleet of vehicles. For instance, each vehicle of the fleet is associated with a polygon corresponding to a service area for that vehicle. A first location may be received from a client computing device, and a set of vehicles of the fleet of vehicles that are currently available to provide transportation services may be identified based on the first location. The polygons associated with each of the set of vehicles may be used to determine a first polygon having a geographic area. A first portion of map information corresponding to the geographic area of the first polygon may be identified, and the first portion may be provided to the client computing device for display to a user such that the portion represents a currently available service area for the user. | 2020-12-31 |
20200410420 | ISSUE RANK MANAGEMENT IN AN ISSUE TRACKING SYSTEM - Described herein is a computer implemented method for selecting a plurality of issues maintained by an issue tracking system for balancing. The method comprises determining a pivot issue maintained by the issue tracking system, adding the pivot issue to a set of issues, the set of issues being issues that are to be balanced, and sequentially analyzing issues in a first direction from the pivot issue, the issues in the first direction having successively adjacent rank values in the first direction to a rank value of the pivot issue and. For each successive first direction issue in the first direction the method further comprises determining whether selection termination criteria are met in respect of the first direction issue and, if not, adding the first direction issue to the set of issues and proceeding to analyse the next issue in the first direction. | 2020-12-31 |
20200410421 | SYSTEM FOR FACILITATING DRIVE UP ORDER FULFILLMENT - A network based order fulfillment systems having an improved user interface at both a customer device and at an order fulfillment location employee device. Both customer and employee devices scan collect user input and other information using one or more sensors of the user devices to provide proper notifications to both the customer and the employee based on the actions of each. Location information for a customer computing device can be used to continually update ETA and time since arrival information displayed at the employee's computing device. | 2020-12-31 |
20200410422 | LABORATORY INSTRUMENT SELECTION AND CONFIGURATION - Laboratory instrument selection and the allocation of reagents to laboratory instruments may be optimized by using computer programs which minimize cost functions relative to various types of constraints. These constraints may include demand for tests, laboratory instrument operational capacity, laboratory instrument reagent capacity, and so on. In some embodiments, this optimization may combine reagent allocation with machine selection to aid in the selection of appropriate laboratory instruments. | 2020-12-31 |
20200410423 | MINING PROCESS LOGS FOR GENERATION OF WORKFLOW FOR SERVICE REQUEST COMPLETION - Generating workflows in response to new service requests. Process logs of different types of prior service requests are mined to extract workflows from the process logs of the prior service requests. The extracted workflows of the prior service requests are saved to a library of workflows. Upon receiving a new service request, one or more workflow recommendations are provided based on one or more of the extracted workflows of the library of workflows. A new workflow is generated for completing the new service request from the one or more workflow recommendations. | 2020-12-31 |
20200410424 | DETERMINING SCHEDULE CONSTRAINTS FROM CONSTRUCTION PLANS - Systems, methods and non-transitory computer readable media for determining schedule constraints from construction plans are provided. For example, at least part of a construction plan for a construction site may be analyzed to identify a first object of a first object type planned to be constructed in the construction site, a first element of a first element type planned to be connected to the first object, and a second element of a second element type planned to be connected to the first object, a plurality of construction tasks for the construction of the first object, and determining a sequence for the plurality of tasks based on the first element type and the second element type. | 2020-12-31 |
20200410425 | GENERATING TASKS FROM IMAGES OF CONSTRUCTION SITES - Systems, methods and non-transitory computer readable media for generating tasks from images of construction sites are provided. For example, image data captured from a construction site using at least one image sensor may be obtained. The image data may be analyzed to determine at least one desired task related to the construction site and to determine at least one parameter of the at least one desired task. The determined at least one parameter of the at least one desired task to provide information configured to cause the performance of the at least one desired task. | 2020-12-31 |
20200410426 | METHOD OF DETERMINING OPTIMAL BUSINESS METRICS FROM A PRODUCT MIX CONSTRAINED BY AT LEAST PHYSICAL SHELF SPACE AND AT LEAST ONE BUSINESS RULE - The present invention relates to a computer implemented method of determining optimal business metrics from a product mix constrained by at least physical shelf space and selectively by at least one business rule. The computer implemented method comprising the steps of defining a boundary constrained shelf space, placing, physically, a product mix within the boundary constrained shelf space, and creating a product mix ranking based, in part, on prior sales of each of the product type. The computer implemented method continues by using a data processing device to develop, through algorithmic autonomous learning, achievable business metric performance of the boundary constrained shelf space. In this regard, a group of similar product mix/ranking is optimized to create an ideal product mix/ranking which is then use to inform changes to make to the product mix to achieve the desired OPTIMAL BUSINESS METRIC. | 2020-12-31 |
20200410427 | REAL-TIME MATCHING AND SMART RECOMMENDATIONS FOR TASKS AND EXPERTS - User information for a particular user is accessed. Expert information for experts and training that is available in an organization of the particular user is accessed. One or more pattern matches between the user information and the expert information are determined. One or more expert recommendations are generated based on the one or more pattern matches and provided. | 2020-12-31 |
20200410428 | CANDIDATE SELECTION USING PERSONALIZED RELEVANCE MODELING SYSTEM - Techniques for selecting candidates using a personalized model are disclosed herein. In some embodiments, a computer system, for each candidate of a plurality of candidates, generating a corresponding confidence score for a combination of the candidate, a particular viewer, and a particular attribute based on a scoring model, with the corresponding confidence score being configured to indicate a likelihood that the particular viewer will select the corresponding candidate as a preference with respect to the particular attribute. The computer system then selects a subset of the plurality of candidates based on the corresponding confidence scores of the candidates in the subset, and causes the subset of candidates to be displayed on a computing device of the viewer along with a prompting for the viewer to select one of the selected subset of candidates as the preference with respect to the particular attribute. | 2020-12-31 |
20200410429 | SYSTEM AND METHOD FOR DEACTIVATING A DRIVER USING A MOBILITY-AS-A-SERVICE SYSTEM - A Mobility-as-a-Service system and related method for deactivating at least one driver of a vehicle includes one or more processors and a memory device in communication with the one or more processors. The memory device stores a driver list generating module, a driver state module, and a deactivation module. The driver list generating module causes the one or more processors to generate a list that includes an identity and an activity state of the at least one driver. The driver state module causes the one or more processors to determine that the at least one driver indicated as active on the list is inactive based on an activity factor. The deactivation module causes the one or more processors to deactivate the at least one driver determined to be inactive. | 2020-12-31 |
20200410430 | OPTIMIZING RESOURCE ALLOCATION FOR AN ORGANIZATION - Embodiments of the present disclosure relate to systems, methods, and user interfaces for optimizing resource allocation for an organization. More particularly, embodiments of the present disclosure utilize multiple data sets to enable organizations to make intuitive business decisions and plan resources accordingly. To do so, various data is collected at a resource engine that utilizes the data to determine resource utilization, occupancy density, and a recommendation. In various embodiments, the resource utilization, occupancy density, and a recommendation may be provided to a user as an alert, a report, or a user interface. The user interface may additionally enable the user to apply the recommendation. In some embodiments, the recommendation may be automatically applied or the user may be directed to perform the recommendation. The alert, report, or user interface may additionally inform the user of the impact of performing or not performing the recommendation. | 2020-12-31 |
20200410431 | INVENTORY COUNT METHOD AND ASSET MANAGEMENT SYSTEM - Example implementations described herein are directed to an asset management system configured to facilitate real-time inventory recognition with image analysis tagged with positional information. The example implementations described herein also provide the method and process to improve the accuracy of the pipe detection for counting with various approaches. Referring the expected number of the pipes, example implementations utilize use the pipe detection algorithm as well as the bounding box of the pipe stack, use the knowledge of the physical size of the pipe stack, and analyze a pipe stack from the images of two directions at the end. | 2020-12-31 |
20200410432 | RFID BASED INVENTORY SYSTEM AND METHOD - An inventory management system and method using RFID tags for the tracking and management of individual items of inventory or work-in-process. The system uses a cloud-based service device to manage inventory at a facility via a communications hub. RFID printers, RFID readers, and antennas located at the facility relay information through the communications hub. Movement of items is monitored by tracking RFID tags associated with each item. Information on the tracked items may be recorded, displayed, and used to generate reports and notifications by the cloud-based services device. Historical tracking records may also be displayed. | 2020-12-31 |
20200410433 | SECURE SMART CONTAINER ASSEMBLY, SYSTEMS, AND METHODS - The disclosed systems and methods provide a smart container or bin. A container bin assembly includes a bin body, a latching mechanism, and controller. A method includes receiving, via a communication interface, an authenticated request to access the smart container, actuating an electromechanical latch to disengage a fastening hook, thereby initiating a mechanical movement of an access component to make an internal compartment accessible, outputting, via an audiovisual element, an alert to identify the container, confirming that the electromechanical latch has re-engaged with the fastening hook, thereby securing the internal compartment, determining a change in a local inventory, and updating the local inventory in a non-volatile data store according to the change. | 2020-12-31 |
20200410434 | MANAGING OBJECTS WITH ASSIGNED STATUS IN AN AUTOMATED TOOL CONTROL SYSTEM - The present application describes an automated inventory control system that comprises one or more storage devices containing a plurality of storage locations for storing objects and first and second predefined locations. The first and second predefined locations for receiving one or more objects includes a sensing system that is configured to sense when an object is deposited at the first and second predefined locations, respectively. The one or more processors are configured to automatically assign a first status to the object and cause transmission of an alert indicating the first status of the deposited object when an object is deposited at the first predefined location. The one or more processors are configured to track a plurality of transactions associated with the deposited object after a user checks the deposited object out of the first predefined location. | 2020-12-31 |
20200410435 | METHOD AND CAMERA SYSTEM FOR MONITORING A PICKING OR PACKAGING PROCESS - A method for monitoring a picking or packaging process of at least one article. Using a plurality of cameras, images are recorded of at least one moving article. Labels in the images are read. The read labels are compared with specifications from a picking list, and a sort of the article is identified on the basis of the comparison. If the sort of the article does not agree with specifications from the picking list, a warning is issued. In addition, a camera system having a plurality of cameras is provided. These cameras are set up to record at least the images and to read labels in the images. | 2020-12-31 |
20200410436 | TRANSPORTATION SYSTEM - A method, system and computer program product includes identifying a presence of a problem with one or more items in transport using cognitive analytics and information received from at least one sensing device. In response to the identifying, storing an identification of the presence of the problem in a block chain ledger by a secure and validated feed having an indexed encryption identifying the sensing device, and recommending a remediation action to fix the problem with the one or more items. | 2020-12-31 |
20200410437 | AVERAGE WEIGHT CALCULATION AND SHIPMENT MANAGEMENT SYSTEM OF EDIBLE POULTRY, AND SHIPMENT MANAGEMENT METHOD USING THE SAME - An average weight calculation and shipment management system of edible poultry includes: a weight measurement device that is installed in a poultry house and measures in real time a weight of edible poultry by using a load cell; a weight and shipment prediction server that receives weight data measured in real time from the weight measurement device, derives an average weight of the edible poultry, and predicts a shipping date by using the derived average weight; and a monitoring unit that is provided with the average weight and the predicted shipping date derived from the weight and shipment prediction server. The weight and shipment prediction server collects a large number of the weight data and densifies the large number of collected weight data to derive an average weight of the edible poultry, and predicts the shipping date by using the derived average weight. | 2020-12-31 |
20200410438 | FILL MODELING FOR HYBRID LAST-MILE DELIVERY - Systems and methods are described for fill modeling in hybrid last-mile deliveries. Hybrid last-mile delivery may refer to delivery of items in which a delivery vehicle meets customers at a pickup location at a specified time. Thus, instead of conventional last-mile delivery in which a delivery vehicle delivers items to an end point, such as a customer home, customers are to meet the delivery vehicle at a specified location and time to pick up items. The systems and methods described herein may include computational modeling of delivery vehicles and items to be delivered in a hybrid last-mile delivery to identify or select delivery vehicles and/or filling the delivery vehicles. | 2020-12-31 |