Patents Examined by Kakali Chaki
  • Patent number: 11694094
    Abstract: In various examples there is a computer-implemented method performed by a digital twin at a computing device in a communications network. The method comprises: receiving at least one stream of event data observed from the environment. Computing at least one schema from the stream of event data, the schema being a concise representation of the stream of event data. Participating in a distributed inference process by sending information about the schema or the received event stream to at least one other digital twin in the communications network and receiving information about schemas or received event streams from the other digital twin. Computing comparisons of the sent and received information. Aggregating the digital twin and the other digital twin, or defining a relationship between the digital twin and the other digital twin on the basis of the comparison.
    Type: Grant
    Filed: March 21, 2018
    Date of Patent: July 4, 2023
    Assignee: SWIM.IT INC
    Inventor: Christopher David Sachs
  • Patent number: 11687823
    Abstract: A computer-implemented method for outputting a data element to a user for an operation by the user to give a label to plural data elements, includes: selecting the data element by either one of a first strategy and a second strategy, the first strategy being a strategy for selecting a data element which has been predicted with a low confidence level, the second strategy being a strategy for selecting a data element which has been predicted with a high confidence level; outputting the selected data element so as for a user to give a label to the selected data element; and switching between the first strategy and the second strategy depending on a progress degree of labeling by the user.
    Type: Grant
    Filed: August 1, 2017
    Date of Patent: June 27, 2023
    Assignee: International Business Machines Corporation
    Inventor: Katsumasa Yoshikawa
  • Patent number: 11687832
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a model using parameter server shards. One of the methods includes receiving, at a parameter server shard configured to maintain values of a disjoint partition of the parameters of the model, a succession of respective requests for parameter values from each of a plurality of replicas of the model; in response to each request, downloading a current value of each requested parameter to the replica from which the request was received; receiving a succession of uploads, each upload including respective delta values for each of the parameters in the partition maintained by the shard; and updating values of the parameters in the partition maintained by the parameter server shard repeatedly based on the uploads of delta values to generate current parameter values.
    Type: Grant
    Filed: August 3, 2020
    Date of Patent: June 27, 2023
    Assignee: Google LLC
    Inventors: Gregory S. Corrado, Kai Chen, Jeffrey A. Dean, Samy Bengio, Rajat Monga, Matthieu Devin
  • Patent number: 11676038
    Abstract: Systems and methods are provided for operating to an initial optimized baseline solution to a multi-objective problem. As the initial optimized baseline solution is determined, some regions, such as local or global maxima, minima, and/or saddle points in the objective space may be mapped. The mapping may be performed by storing mesh chromosomes corresponding to some of the features (e.g., extrema, saddle points, etc.) in the objective space along with the location of those chromosomes within the objective space (e.g., objective values corresponding to each of the objectives). The mesh chromosome may be used in subsequent re-optimization problems, such as with reformulation. Although in a re-optimization the objectives, decision variables, and or objective/constraint models may change, the mesh chromosomes may still provide information and direction for more quickly and/or with reduced resources converge on a re-optimized solution.
    Type: Grant
    Filed: September 16, 2016
    Date of Patent: June 13, 2023
    Assignee: THE AEROSPACE CORPORATION
    Inventor: Timothy Guy Thompson
  • Patent number: 11669776
    Abstract: In an embodiment, a method for optimizing computer machine learning includes receiving an optimization goal. The optimization goal is used to search a database of base option candidates (BOC) to identify matching BOCs that at least in part matches the goal. A selection of a selected base option among the matching BOCs is received. Machine learning prediction model(s) are selected based at least in part on the goal to determine prediction values associated with alternative features for the selected base option, where the model(s) were trained using training data to at least identify weight values associated with the alternative features for models. Based on the prediction values, at least a portion of the alternative features is sorted to generate an ordered list. The ordered list is provided for use in manufacturing an alternative version of the selected base option with the alternative feature(s) in the ordered list.
    Type: Grant
    Filed: August 27, 2021
    Date of Patent: June 6, 2023
    Assignee: Stitch Fix, Inc.
    Inventors: Erin S. Boyle, Daragh Sibley
  • Patent number: 11669731
    Abstract: Described is a system for controlling a mobile platform. A neural network that runs on the mobile platform is trained based on a current state of the mobile platform. A Satisfiability Modulo Theories (SMT) solver capable of reasoning over non-linear activation functions is periodically queried to obtain examples of states satisfying specified constraints of the mobile platform. The neural network is then trained on the examples of states. Following training on the examples of states, the neural network selects an action to be performed by the mobile platform in its environment. Finally, the system causes the mobile platform to perform the selected action in its environment.
    Type: Grant
    Filed: November 21, 2019
    Date of Patent: June 6, 2023
    Assignee: HRL LABORATORIES, LLC
    Inventors: Michael A. Warren, Christopher Serrano
  • Patent number: 11670100
    Abstract: A system and method for training a system for monitoring administration of medication. The method includes the steps of a method for training a medication administration monitoring apparatus, comprising the steps of defining one or more predetermined medications and then acquiring information from one or more data sources of a user administering medication. A first network is trained to recognize a first step of a medication administration sequence, and then a second network is trained to recognize a second step of a medication administration sequence based upon the training of the first network.
    Type: Grant
    Filed: August 21, 2019
    Date of Patent: June 6, 2023
    Assignee: AIC Innovations Group, Inc.
    Inventors: Lei Guan, Dehua Lai
  • Patent number: 11669744
    Abstract: A method for receiving training data for training a neural network (NN) to perform a machine learning (ML) task and for determining, using the training data, an optimized NN architecture for performing the ML task is described. Determining the optimized NN architecture includes: maintaining population data comprising, for each candidate architecture in a population of candidate architectures, (i) data defining the candidate architecture, and (ii) data specifying how recently a neural network having the candidate architecture has been trained while determining the optimized neural network architecture; and repeatedly performing multiple operations using each of a plurality of worker computing units to generate a new candidate architecture based on a selected candidate architecture having the best measure of fitness, adding the new candidate architecture to the population, and removing from the population the candidate architecture that was trained least recently.
    Type: Grant
    Filed: September 14, 2021
    Date of Patent: June 6, 2023
    Assignee: Google LLC
    Inventors: Yanping Huang, Alok Aggarwal, Quoc V. Le, Esteban Alberto Real
  • Patent number: 11663535
    Abstract: An example method includes receiving, by one or more processors, a representation of an utterance spoken at a computing device; identifying, by a first computational agent from a plurality of computational agents and based on the utterance, a multi-element task to be performed, wherein the plurality of computational agents includes one or more first party computational agents and a plurality of third-party computational agents; and performing, by the first computational agent, a first sub-set of elements of the multi-element task, wherein performing the first sub-set of elements comprises selecting a second computational agent from the plurality of computational agents to perform a second sub-set of elements of the multi-element task.
    Type: Grant
    Filed: November 16, 2017
    Date of Patent: May 30, 2023
    Assignee: GOOGLE LLC
    Inventors: Robert Stets, Valerie Nygaard, Bogdan Caprita, Bradley M. Abrams, Jason Brant Douglas
  • Patent number: 11663483
    Abstract: According to embodiments, an encoder neural network receives a one-hot representation of a real text. The encoder neural network outputs a latent representation of the real text. A decoder neural network receives random noise data or artificial code generated by a generator neural network from random noise data. The decoder neural network outputs softmax representation of artificial text. The decoder neural network receives the latent representation of the real text. The decoder neural network outputs a reconstructed softmax representation of the real text. A hybrid discriminator neural network receives a first combination of the soft-text and the latent representation of the real text and a second combination of the softmax representation of artificial text and the artificial code. The hybrid discriminator neural network outputs a probability indicating whether the second combination is similar to the first combination. Additional embodiments for utilizing latent representation are also disclosed.
    Type: Grant
    Filed: October 30, 2018
    Date of Patent: May 30, 2023
    Assignee: Huawei Technologies Co., Ltd.
    Inventors: Md Akmal Haidar, Mehdi Rezagholizadeh
  • Patent number: 11657294
    Abstract: Techniques are provided for evolutionary computer-based optimization and artificial intelligence systems, and include receiving first and second candidate executable code (with ploidy of at least two and one, respectively) each selected at least in part based on a fitness score. If the desired ploidy of the resultant executable code is one, then the first candidate executable code and the second candidate executable code are combined to produce haploid executable code. If the desired ploidy is two, then the first candidate executable code and the second candidate executable code are combined to produce diploid executable code. A fitness score is determined for the resultant executable code, and a determination is made whether the resultant executable code will be used as a future candidate executable code based at least in part on the third fitness score. If an exit condition is met, then the resultant executable code is used as evolved executable code.
    Type: Grant
    Filed: May 12, 2021
    Date of Patent: May 23, 2023
    Assignee: Diveplane Corporation
    Inventor: Christopher James Hazard
  • Patent number: 11651241
    Abstract: A method of controlling an operational system by a rules management system comprising a processor and a memory, and a computing apparatus comprising a processor and a memory are provided. The processor is programmed to execute rules from a rules repository stored on a memory in response to a request. The computing apparatus further comprises a high rules repository storing one or more high level rules, wherein each high level rule, when executed by the processor, modifies the effect of execution of one or more rules Rm in the rules repository; and a high rules conditions module that when executed by the processor identifies and executes the high level rules that apply to the request.
    Type: Grant
    Filed: October 10, 2018
    Date of Patent: May 16, 2023
    Assignee: MASTERCARD INTERNATIONAL INCORPORATED
    Inventor: Muhammad Yaseen Ali
  • Patent number: 11651271
    Abstract: Computer systems and associated methods are disclosed to detect a future change point in time series data used as input to a machine learning model. A forecast for the time series data is generated. In some embodiments, a fitting model is generated from the time series data, and residuals of the fitting model are obtained for respective portions of the data both before and after a potential change point in the future. The change point is determined based on a ratio of residual metrics for the two portions. In some embodiments, data features are extracted from individual segments in the time series data, and the segments are clustered based on their data features. A change point is determined based on a dissimilarity in cluster assignments for segments before and after the point. In some embodiments, when a change point is predicted, an update of the machine learning model is triggered.
    Type: Grant
    Filed: July 3, 2018
    Date of Patent: May 16, 2023
    Assignee: Amazon Technologies, Inc.
    Inventors: Quiping Xu, Joshua Allen Edgerton
  • Patent number: 11651227
    Abstract: In general, the disclosure describes techniques for facilitating trust in neural networks using a trusted neural network system. For example, described herein are multi-headed, trusted neural network systems that can be trained to satisfy one or more constraints as part of the training process, where such constraints may take the form of one or more logical rules and cause the objective function of at least one the heads of the trusted neural network system to steer, during machine learning model training, the overall objective function for the system toward an optimal solution that satisfies the constraints. The constraints may be non-temporal, temporal, or a combination of non-temporal and temporal. The constraints may be directly compiled to a neural network or otherwise used to train the machine learning model.
    Type: Grant
    Filed: December 19, 2018
    Date of Patent: May 16, 2023
    Assignee: SRI INTERNATIONAL
    Inventors: Shalini Ghosh, Patrick Lincoln, Ashish Tiwari, Susmit Jha
  • Patent number: 11645546
    Abstract: A system and method for predicting multi-agent locations is disclosed herein. A computing system retrieves tracking data from a data store. The computing system generates a predictive model using a conditional variational autoencoder. The conditional variational autoencoder learns one or more paths a subset of agents of the plurality of agents are likely to take. The computing system receives tracking data from a tracking system positioned remotely in a venue hosting a candidate sporting event. The computing system identifies one or more candidate agents for which to predict locations. The computing system infers, via the predictive model, one or more locations of the one or more candidate agents. The computing system generates a graphical representation of the one or more locations of the one or more candidate agents.
    Type: Grant
    Filed: January 22, 2019
    Date of Patent: May 9, 2023
    Assignee: STATS LLC
    Inventors: Panna Felsen, Sujoy Ganguly, Patrick Lucey
  • Patent number: 11636344
    Abstract: During training of deep neural networks, a Copernican loss (LC) is designed to augment the standard Softmax loss to explicitly minimize intra-class variation and simultaneously maximize inter-class variation. Copernican loss operates using the cosine distance and thereby affects angles leading to a cosine embedding, which removes the disconnect between training and testing.
    Type: Grant
    Filed: March 12, 2019
    Date of Patent: April 25, 2023
    Assignee: Carnegie Mellon University
    Inventors: Marios Savvides, Dipan Kumar Pal
  • Patent number: 11636317
    Abstract: Long-short term memory (LSTM) cells on spiking neuromorphic hardware are provided. In various embodiments, such systems comprise a spiking neurosynaptic core. The neurosynaptic core comprises a memory cell, an input gate operatively coupled to the memory cell and adapted to selectively admit an input to the memory cell, and an output gate operatively coupled to the memory cell an adapted to selectively release an output from the memory cell. The memory cell is adapted to maintain a value in the absence of input.
    Type: Grant
    Filed: February 16, 2017
    Date of Patent: April 25, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Rathinakumar Appuswamy, Michael Beyeler, Pallab Datta, Myron Flickner, Dharmendra S. Modha
  • Patent number: 11630987
    Abstract: Technologies for a neural belief reasoner model generative models that specifies belief functions are described. Aspects include receiving, by a device operatively coupled to a processor, a request for a belief function, and processing, by the device, the request for the belief function in the generative model based on trained probability parameters and a minimization function to determine a generalized belief function defined by fuzzy sets. Data corresponding to the generalized belief function is output, e.g., as a belief value and plausibility value.
    Type: Grant
    Filed: April 30, 2018
    Date of Patent: April 18, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventor: Haifeng Qian
  • Patent number: 11625612
    Abstract: The domain adaptation problem is addressed by using the predictions of a trained model over both source and target domain to retain the model with the assistance of an auxiliary model and a modified objective function. Inaccuracy in the model's predictions in the target domain is treated as noise and is reduced by using a robust learning framework during retraining, enabling unsupervised training in the target domain. Applications include object detection models, where noise in retraining is reduced by explicitly representing label noise and geometry noise in the objective function and using the ancillary model to inject information about label noise.
    Type: Grant
    Filed: January 31, 2020
    Date of Patent: April 11, 2023
    Assignee: D-WAVE SYSTEMS INC.
    Inventors: Arash Vahdat, Mani Ranjbar, Mehran Khodabandeh, William G. Macready, Zhengbing Bian
  • Patent number: 11625595
    Abstract: Knowledge transfer between recurrent neural networks is performed by obtaining a first output sequence from a bidirectional Recurrent Neural Network (RNN) model for an input sequence, obtaining a second output sequence from a unidirectional RNN model for the input sequence, selecting at least one first output from the first output sequence based on a similarity between the at least one first output and a second output from the second output sequence; and training the unidirectional RNN model to increase the similarity between the at least one first output and the second output.
    Type: Grant
    Filed: August 29, 2018
    Date of Patent: April 11, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Gakuto Kurata, Kartik Audhkhasi