Patents Examined by Wilbert L. Starks
  • Patent number: 11562288
    Abstract: Techniques for hosting adding and warming a host are described. In some instances, a method of determining that at least one group of hosts is to be increased by adding an additional host to the group of hosts; sending a request to the group of hosts for a list of machine learning models loaded per host of the group of hosts; receiving, from each host, the list of loaded machine learning models; loading at least a proper subset of list of loaded machine learning models into random access memory of the at least one group; receiving a request to perform an inference; routing the request to the additional host of the group of hosts; performing an inference using the additional host of the group of hosts; and providing a result of the inference to an external entity is described.
    Type: Grant
    Filed: September 28, 2018
    Date of Patent: January 24, 2023
    Assignee: Amazon Technologies, Inc.
    Inventors: Enrico Sartorello, Stefano Stefani, Nikhil Kandoi, Rama Krishna Sandeep Pokkunuri, Kalpesh N. Sutaria, Navneet Sabbineni, Ganesh Kumar Gella, Cheng Ran Li
  • Patent number: 11556811
    Abstract: Provided is an information processing apparatus, including a calculation section which calculates a proficiency level of a user for operations performed by the user for achieving a prescribed objective based on history information related to the operations and attribute information related to physical features of the user, and a generation section which generates advice for achieving the objective based on the proficiency level calculated by the calculation section.
    Type: Grant
    Filed: July 16, 2019
    Date of Patent: January 17, 2023
    Inventors: Takayasu Kon, Yoichiro Sako, Kazunori Hayashi, Yasunori Kamada, Takatoshi Nakamura, Hiroyuki Hanaya, Tomoya Onuma, Akira Tange
  • Patent number: 11526150
    Abstract: In an example, a method includes receiving object model data describing at least a portion of an object to be generated by additive manufacturing. Object generation instructions for generating the object in its entirety may be derived based on the object model data. Where it is determined that the object model data comprises a data deficiency for deriving the object generation instructions, at least one attribute for the object may be inferred and object generation instructions may be derived based on the object model data and the inferred attribute.
    Type: Grant
    Filed: July 28, 2017
    Date of Patent: December 13, 2022
    Assignee: Hewlett-Packard Development Company, L.P.
    Inventors: Vanessa Verzwyvelt, Matthew A Shepherd, Morgan T Schramm, Andrew E Fitzhugh, Lihua Zhao, Jacob Tyler Wright, Hector Lebron
  • Patent number: 11526787
    Abstract: The present invention discloses a knowledge inference engine system and a method of implementation, relating to the field of wired communication networking technology. The system includes a data generation module, a stream partitioning model, an offline scheduling module, an online scheduling module, a scheduling solution base and a historical information base module, the data generation module used to generate a dataset and divide it into a plurality of partitions, the stream partitioning model used to partition the dataset, the offline scheduling module used to generate a scheduling solution for static network requirements, the online scheduling module used to rapidly generate a scheduling solution for a new TT stream, the scheduling solution base used to store a result of the partitioning, an iterative scheduling order for the partitions and the offline scheduling solution, the historical information base used to update and store relevant data information and performance indicators.
    Type: Grant
    Filed: June 17, 2022
    Date of Patent: December 13, 2022
    Assignee: Shanghai Jiao Tong University
    Inventors: Cailian Chen, Qimin Xu, Shouliang Wang, Shanying Zhu, Lei Xu, Xinping Guan
  • Patent number: 11507587
    Abstract: Exemplary systems and methods for allocating capital to trading strategies may include a means for generating a virtual machine for a trading strategy in a historical server, a means for obtaining historical performance data for the trading strategy from the historical server, a means for transforming the historical performance data into metrical data, a means for transforming the historical performance data and metrical data into a neural network usable data set, a means for creating a neural network base, and a means for forming a neural network.
    Type: Grant
    Filed: February 28, 2020
    Date of Patent: November 22, 2022
    Assignee: Capitalogix IP Owner, LLC
    Inventors: Howard M. Getson, Sean Vallie, Adam Peterson, Kelvin Rodriguez
  • Patent number: 11501140
    Abstract: Hardware neural network processors, are provided. A neural core includes a weight memory, an activation memory, a vector-matrix multiplier, and a vector processor. The vector-matrix multiplier is adapted to receive a weight matrix from the weight memory, receive an activation vector from the activation memory, and compute a vector-matrix multiplication of the weight matrix and the activation vector. The vector processor is adapted to receive one or more input vector from one or more vector source and perform one or more vector functions on the one or more input vector to yield an output vector. In some embodiments a programmable controller is adapted to configure and operate the neural core.
    Type: Grant
    Filed: June 19, 2018
    Date of Patent: November 15, 2022
    Inventors: Andrew S. Cassidy, Rathinakumar Appuswamy, John V. Arthur, Pallab Datta, Steven K. Esser, Myron D. Flickner, Jennifer Klamo, Dharmendra S. Modha, Hartmut Penner, Jun Sawada, Brian Taba
  • Patent number: 11468329
    Abstract: Examples include techniques to manage training or trained models for deep learning applications. Examples include routing commands to configure a training model to be implemented by a training module or configure a trained model to be implemented by an inference module. The commands routed via out-of-band (OOB) link while training data for the training models or input data for the trained models are routed via inband links.
    Type: Grant
    Filed: September 18, 2020
    Date of Patent: October 11, 2022
    Assignee: Intel Corporation
    Inventors: Francesc Guim Bernat, Suraj Prabhakaran, Kshitij A. Doshi, Da-Ming Chiang
  • Patent number: 11468366
    Abstract: Example systems and methods of developing a learning model are presented. In one example, a sample data set to train a first learning algorithm is accessed. A number of states for each input of the sample data set is determined. A subset of the inputs is selected, and the sample data set is partitioned into a number of partitions equal to a combined number of states of the selected inputs. A second learning algorithm is created for each of the partitions, wherein each second learning algorithm receives the unselected inputs. Each of the second learning algorithms is assigned to a processor and trained using the samples of the partition corresponding to that algorithm. Decision logic is generated to direct each of a plurality of operational data units as input to one of the second learning algorithms based on states of the selected inputs of the operational data unit.
    Type: Grant
    Filed: October 9, 2019
    Date of Patent: October 11, 2022
    Assignee: SAP SE
    Inventors: Bin Qin, Farooq Azam, Denis Malov
  • Patent number: 11449794
    Abstract: Language-based machine learning approach for automatically detecting universal charset and the language of a received document is disclosed. The language-based machine learning approach employs a plurality of text document samples in different languages, after converting them to a selected Unicode style (if their original encoding schemes are not the selected Unicode), to generate a plurality of language-based machine learning models during the training stage. During the application stage, vector representations of the received document for different combinations of charsets and their respective applicable languages are tested against the plurality of machine learning models to ascertain the charset and language combination that is most similar to its associated machine learning model, thereby identifying the charset and language of the received document.
    Type: Grant
    Filed: August 21, 2019
    Date of Patent: September 20, 2022
    Assignee: Trend Micro Incorporated
    Inventor: Lili Diao
  • Patent number: 11442803
    Abstract: An approach is provided for detecting and analyzing an anomaly in application performance in a client-server connection via a network. A status code of a response sent by a server to a client, a round trip latency time (RTT) of the response, and a time out of a connection between client and server are determined. Using a k-means clustering algorithm, buckets of RTT values clustered into lower and higher values, and running counts and means for the RTT values in each bucket, an RTT value is determined to exceed a threshold value. Based on the status code, the RTT value exceeding the threshold, and the connection time out, the anomaly is detected. Based on temporal and textual analyses of log entries and an environment analysis, candidate root causes of a failure that resulted in the anomaly are determined.
    Type: Grant
    Filed: February 27, 2019
    Date of Patent: September 13, 2022
    Assignee: KYNDRYL, INC.
    Inventors: Luba Cherbakov, Kuntal Dey, Sougata Mukherjea, Nitendra Rajput, Venkatraman Ramakrishna
  • Patent number: 11416769
    Abstract: Approaches presented herein enable intelligent service request classification and assignment learning. More specifically, a request comprising a free form text or spoken description is received from a user. The request description is parsed and classified by a regression-based classifier. The regression-based classifier classifies based on, for example: the description itself; the requestor's history of requests, and/or supplemental demographics about a requestor. Optionally, a user may verify the classification or select from a plurality of returned classifications. A service provider or administrator confirms that a classification is correct. If not, the incorrectly classified request is queued. If so, the correctly classified request is added to a set of training data to be used in classifying future requests.
    Type: Grant
    Filed: April 17, 2019
    Date of Patent: August 16, 2022
    Assignee: KYNDRYL, INC.
    Inventor: Tyson R. Midboe
  • Patent number: 11341401
    Abstract: Embodiments of the invention relate to a neural network system for simulating neurons of a neural model. One embodiment comprises a memory device that maintains neuronal states for multiple neurons, a lookup table that maintains state transition information for multiple neuronal states, and a controller unit that manages the memory device. The controller unit updates a neuronal state for each neuron based on incoming spike events targeting said neuron and state transition information corresponding to said neuronal state.
    Type: Grant
    Filed: February 28, 2019
    Date of Patent: May 24, 2022
    Assignee: International Business Machines Corporation
    Inventors: Rodrigo Alvarez-Icaza Rivera, John V. Arthur, Andrew S. Cassidy, Pallab Datta, Paul A. Merolla, Dharmendra S. Modha
  • Patent number: 11308170
    Abstract: Embodiments are directed to data verification of business or consumer data. Certain embodiments include a data verification system that receives or selects data to be verified, selects one or more verification methods to verify, update, and/or append/enhance the data. The data verification system may verify the data with one or more data verification methods, either alone or in combination. The methods may include a web-crawling verification method, an agent web verification method, a call verification method, a direct mail method, an email method, an in-person verification method, or other methods. The system has the ability to, automatically or manually, (1) blend automatic and manual segmentation of records or elements by criteria such as industry type, best times of day/month/year to verify, update, or append, cost, and level of importance (2) select the best verification processing method(s), and (3) manage the results and properly verify, update, append/enhance records.
    Type: Grant
    Filed: October 3, 2019
    Date of Patent: April 19, 2022
    Assignee:, Inc.
    Inventors: Albert Chia-Shu Chang, Gregory Dean Jones, Carolyn Paige Soltes Matthies
  • Patent number: 11281979
    Abstract: A model identification system includes a device information acquiring unit that acquires device information used to identify a model of an electric device, an operation extracting unit that extracts data of a predetermined operation section, a feature quantity extracting unit that extracts a parameter used to identify the electric device, and a model identifying unit that identifies a model of an electric device, wherein the feature quantity extracting unit performs a machine learning process by sampling the data of the predetermined operation section extracted from the operation extracting unit a plurality of times, extracts a parameter corresponding to each sampling, and extracts a parameter appropriate to identify a model among a plurality of sampled parameters.
    Type: Grant
    Filed: January 23, 2017
    Date of Patent: March 22, 2022
    Assignee: Informetis Corporation
    Inventors: Nobuaki Hiratsuka, Masato Ito
  • Patent number: 11250346
    Abstract: A method for rejecting biased data using a machine learning model includes receiving a cluster training data set including a known unbiased population of data and training a clustering model to segment the received cluster training data set into clusters based on data characteristics of the known unbiased population of data. Each cluster of the cluster training data set includes a cluster weight. The method also includes receiving a training data set for a machine learning model and generating training data set weights corresponding to the training data set for the machine learning model based on the clustering model. The method also includes adjusting each training data set weight of the training data set weights to match a respective cluster weight and providing the adjusted training data set to the machine learning model as an unbiased training data set.
    Type: Grant
    Filed: September 10, 2018
    Date of Patent: February 15, 2022
    Assignee: Google LLC
    Inventors: Christopher Farrar, Steven Ross
  • Patent number: 11244250
    Abstract: The invention relates to a system and to a method for determining a state of a device by means of a trained support-vector machine. According to the invention, an operating parameter space is divided into classification volumes, at least one of which indicates a normal state and at least one other of which indicates a fault state of the device. A current state of the device can therefore be determined by determining where a current operating parameter point is to be arranged in the operating parameter space. The invention further relates to methods and to variants of the system in order to facilitate a cause evaluation and to determine particularly relevant operating parameters for the fault determination.
    Type: Grant
    Filed: August 15, 2019
    Date of Patent: February 8, 2022
    Inventor: Jonas Deichmann
  • Patent number: 11227223
    Abstract: A computing system trains a classification model using distributed training data. In response to receipt of a first request, a training data subset is accessed and sent to each higher index worker computing device, the training data subset sent by each lower index worker computing device is received, and a first kernel matrix block and a second kernel matrix block are computed using a kernel function and the accessed or received training data subsets. (A) In response to receipt of a second request from the controller device, a first vector is computed using the first and second kernel matrix blocks, a latent function vector and an objective function value are computed, and the objective function value is sent to the controller device. (A) is repeated until the controller device determines training of a classification model is complete. Model parameters for the trained classification model are output.
    Type: Grant
    Filed: July 7, 2021
    Date of Patent: January 18, 2022
    Assignee: SAS Institute Inc.
    Inventor: Yingjian Wang
  • Patent number: 11222271
    Abstract: Embodiments for planning vehicular driving actions in the presence of non-recurrent events by a processor. One or more dynamics of non-recurrent events in a transport network may be learned according to one or more contextual factors. One or more vehicle-specific factors may be learned in relation to a historical journey and current journey of a vehicle. One or more action responses associated with the one or more non-recurrent events and the one or more vehicle-specific factors may be generated.
    Type: Grant
    Filed: April 19, 2018
    Date of Patent: January 11, 2022
    Inventors: Julien Monteil, Anton Dekusar, Yassine Lassoued, Rodrigo H. Ordonez-Hurtado, Giovanni Russo, Martin Mevissen
  • Patent number: 11216737
    Abstract: Recommendations for new experiments are generated via a pipeline that includes a predictive model and a preference procedure. In one example, a definition of a development task includes experiment parameters that may be varied, the outcomes of interest and the desired goals or specifications. Existing experimental data is used by machine learning algorithms to train a predictive model. The software system generates candidate experiments and uses the trained predictive model to predict the outcomes of the candidate experiments based on their parameters. A merit function (referred to as a preference function) is calculated for the candidate experiments. The preference function is a function of the experiment parameters and/or the predicted outcomes. It may also be a function of features that are derived from these quantities. The candidate experiments are ranked based on the preference function.
    Type: Grant
    Filed: August 17, 2018
    Date of Patent: January 4, 2022
    Assignee: Uncountable Inc.
    Inventors: Jason Isaac Hirshman, Noel Hollingsworth, Will Tashman
  • Patent number: 11205135
    Abstract: The Quanton virtual machine approximates solutions to NP-Hard problems in factorial spaces in polynomial time. The data representation and methods emulate quantum computing on classical hardware but also implement quantum computing if run on quantum hardware. The Quanton uses permutations indexed by Lehmer codes and permutation-operators to represent quantum gates and operations. A generating function embeds the indexes into a geometric object for efficient compressed representation. A nonlinear directional probability distribution is embedded to the manifold and at the tangent space to each index point is also a linear probability distribution. Simple vector operations on the distributions correspond to quantum gate operations. The Quanton provides features of quantum computing: superpositioning, quantization and entanglement surrogates. Populations of Quantons are evolved as local evolving gate operations solving problems or as solution candidates in an Estimation of Distribution algorithm.
    Type: Grant
    Filed: September 6, 2019
    Date of Patent: December 21, 2021
    Assignee: KYNDI, INC.
    Inventor: Arun Majumdar