Patents Examined by Wilbert L. Starks
  • Patent number: 11972363
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a plurality of model representations of predictive models, each model representation associated with a respective user and expresses a respective predictive model, and selecting a model implementation for each of the model representations based on one or more system usage properties associated with the user associated with the corresponding model representation.
    Type: Grant
    Filed: May 22, 2020
    Date of Patent: April 30, 2024
    Assignee: Google LLC
    Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
  • Patent number: 11960976
    Abstract: Embodiments relate to intelligent entities for providing information service over a network in a telecommunication system. An intelligent element framework manages intelligent entities, which are modular and trained using artificial intelligence or machine learning algorithms to perform prediction or inference for different types of applications. The intelligent entities may communicate with each other via the intelligent element framework. For example, an intelligent entity may generate an output and provide the output for use by one or more other intelligent entities. Thus, the intelligent element framework may distribute portions of tasks for information service across multiple intelligent entities chained together, for example, in a directed graph configuration.
    Type: Grant
    Filed: September 4, 2018
    Date of Patent: April 16, 2024
    Assignee: B.YOND, INC.
    Inventors: Johnny Ghibril, Baris Abaci
  • Patent number: 11948099
    Abstract: Implementations include providing, by the PKG platform, an initial knowledge graph based on user-specific data associated with a user, and a domain-specific knowledge graph, receiving, by the PKG platform, data representative of at least one answer provided from the user to a respective question, providing, by the PKG platform, an expanded knowledge graph based on the initial knowledge graph, the expanded knowledge graph including one or more nodes and respective edges based on the data, generating, by the PKG platform, a weighted knowledge graph based a groundtruth knowledge graph, and a targeted knowledge graph, the groundtruth knowledge graph including one or more true answers, and the targeted knowledge graph including the at least one answer provided from the user, and generating, by the PKG platform, the hyper-personalized knowledge graph (hpKG) based on the weighted knowledge graph, the hpKG being unique to the user within a domain.
    Type: Grant
    Filed: August 30, 2021
    Date of Patent: April 2, 2024
    Assignee: Accenture Global Solutions Limited
    Inventors: Freddy Lecue, Christophe Dominique Marie Gueret, Diarmuid John Cahalane
  • Patent number: 11948078
    Abstract: The disclosure provides a framework or system for learning visual representation using a large set of image/text pairs. The disclosure provides, for example, a method of visual representation learning, a joint representation learning system, and an artificial intelligence (AI) system that employs one or more of the trained models from the method or system. The AI system can be used, for example, in autonomous or semi-autonomous vehicles. In one example, the method of visual representation learning includes: (1) receiving a set of image embeddings from an image representation model and a set of text embeddings from a text representation model, and (2) training, employing mutual information, a critic function by learning relationships between the set of image embeddings and the set of text embeddings.
    Type: Grant
    Filed: August 21, 2020
    Date of Patent: April 2, 2024
    Assignee: NVIDIA Corporation
    Inventors: Arash Vahdat, Tanmay Gupta, Xiaodong Yang, Jan Kautz
  • Patent number: 11934963
    Abstract: According to one embodiment, an information processing method classifies an instance including a combination of data items of subclasses of either physical world classes describing physical entities or cyber world classes describing concepts. The information processing method comprises the steps of: obtaining first data including the instance; and inferring and determining a subclass the instance belongs to by referring to at least either definition data or log data. The definition data defines the subclasses. The log data includes a set of the first data obtained in the past, each of the first data including the instance with the corresponding subclass defined in the definition data.
    Type: Grant
    Filed: March 11, 2019
    Date of Patent: March 19, 2024
    Assignee: Kabushiki Kaisha Toshiba
    Inventor: Lan Wang
  • Patent number: 11922284
    Abstract: There is a need for solutions that generates a temporally dynamic prediction for a particular prediction input. This need can be addressed by, for example, processing the prediction input using each of a plurality of temporally trained machine learning models to generate a corresponding model-specific prediction inference of a plurality of model-specific prediction inferences and processing the plurality of model-specific prediction inferences using an ensemble model to generate the temporally dynamic prediction for the prediction input.
    Type: Grant
    Filed: June 20, 2019
    Date of Patent: March 5, 2024
    Assignee: LIBERTY MUTUAL INSURANCE COMPANY
    Inventors: Timothy Jonathan Pirozzi, Peter William Dowling, Amarnauth Sukhu, Peter Alexander Salem, Jr., Ryan Patrick O'Neill, Lindsey Marie Marley
  • Patent number: 11924290
    Abstract: An information handling system operating a sensor fusion prediction based automatic adjustment system may comprise sensors measuring influencing attributes comprising information handling system operational values, wherein a subset of the influencing attributes influence one of a plurality of system characteristics, and a memory storing definitions of a user behavior characteristic, a performance mapping characteristic, a power status characteristic, a security profile characteristic, and a policy configuration characteristic.
    Type: Grant
    Filed: October 26, 2018
    Date of Patent: March 5, 2024
    Assignee: Dell Products, LP
    Inventors: Abeye Teshome, Sinem Gulbay
  • Patent number: 11915127
    Abstract: A system includes first, second and third input data sets. The first input data set includes demographic information characterizing a patient. The second and third input data sets characterize a healthcare treatment history of the patient. A neural network includes first, second and third neural subnetworks. The first neural subnetwork is configured to process the first input data set to produce a first output data set. The second neural subnetwork is configured to process the second input data set to produce a second output data set. The third neural subnetwork is configured to process the third input data set to produce a third output data set. An autoencoder layer has an input layer comprising the first, second and third output data sets and is configured to process the first, second and third output data sets to produce a secondary output data set.
    Type: Grant
    Filed: August 3, 2019
    Date of Patent: February 27, 2024
    Assignee: Edifecs, Inc.
    Inventors: Kanaka Prasad Saripalli, Frank Lucas Wolcott, Paul Raymond Dausman, Shailly Saxena, William Lee Clements
  • Patent number: 11915111
    Abstract: A federated machine learning system for training students comprises a first adaptive training system having a first artificial intelligence module for adapting individualized training to a first group of students and for developing a first learning model based on a first set of learning performance metrics. A second adaptive training system provides individualized training to a second group of students and has a data property extraction module for extracting statistical properties from a second set of learning performance metrics for the second group of students. A data simulator module generates simulated performance metrics using extracted statistical properties from the second set of learning performance metrics to thereby generate a second learning model. A federation computing device receives first and second model weights for the first and second learning models and generates or refines a federated model based on the first and second model weights.
    Type: Grant
    Filed: March 15, 2023
    Date of Patent: February 27, 2024
    Assignee: CAE INC.
    Inventors: Jean-Fran├žois Delisle, Ben Winokur, Navpreet Singh
  • Patent number: 11915104
    Abstract: Respective correlation metrics between token groups of a particular text attribute of a data set and a prediction target attribute are computed. Based on the correlation metrics, a predictive token group list is created. For various observation records of the data set, values of a derived categorical attribute corresponding to the particular text attribute are determined based on matches between the particular text attribute value and the predictive token group list. A measure of the predictive utility of the particular text attribute is obtained using correlations between the categorical attribute and the prediction target attribute.
    Type: Grant
    Filed: November 1, 2019
    Date of Patent: February 27, 2024
    Assignee: Amazon Technologies, Inc.
    Inventors: Gowda Dayannda Anjaneyapura Range, Rajeev Ramnarain Rastogi
  • Patent number: 11900274
    Abstract: Disclosed systems, methods, and computer readable media can detect an association between semantic entities and generate semantic information between entities. For example, semantic entities and associated semantic collections present in knowledge bases can be identified. A time period can be determined and divided into time slices. For each time slice, word embeddings for the identified semantic entities can be generated; a first semantic association strength between a first semantic entity input and a second semantic entity input can be determined; and a second semantic association strength between the first semantic entity input and semantic entities associated with a semantic collection that is associated with the second semantic entity can be determined. An output can be provided based on the first and second semantic association strengths.
    Type: Grant
    Filed: July 7, 2021
    Date of Patent: February 13, 2024
    Assignee: nference, Inc.
    Inventors: Murali Aravamudan, Venkataramanan Soundararajan, Ajit Rajasekharan
  • Patent number: 11899787
    Abstract: To provide a robust information processing system against attacks by Adversarial Example. A neural network model 608, a latent space database 609 for storing position information in a latent space in which first output vectors, which are output vectors of a predetermined hidden layer included in the neural network model, are embedded concerning input data used for learning of the neural network model, and an inference control unit 606 for making an inference using the neural network model and the latent space database are provided. The inference control unit infers the input data based on the positional relationship between the second output vector, which is an output vector of the predetermined hidden layer concerning input data to be inferred, and the first output vectors in said latent space.
    Type: Grant
    Filed: March 16, 2020
    Date of Patent: February 13, 2024
    Assignee: HITACHI, LTD.
    Inventor: Tadayuki Matsumura
  • Patent number: 11893508
    Abstract: An analytics server for scalable predictive analysis for analytics as a software service in multi-tenant environment is provided. The analytics server automatically validates portability of a predictive model from a first tenant to a second tenant by comparing value distribution of parameters between data inputs of the first tenant and the second tenant. The analytics server further automatically detects source data changes over a configurable time horizon as relevant to predictive model inputs, by comparing value distribution of parameters between two data inputs from a same tenant separated by a selected time horizon.
    Type: Grant
    Filed: June 8, 2021
    Date of Patent: February 6, 2024
    Assignee: Digital.ai Software, Inc.
    Inventors: Rahul Kapoor, Joseph Patrick Foley, Abhijeet Anant Joshi
  • Patent number: 11886958
    Abstract: Systems and methods for automated data extraction and adaptation are disclosed. The system may receive a data input from an external source using various different input channels. The system may determine a data quality of the data input by comparing data fields of the data input to known metadata in the system. The system may reformat the data input based on the comparison to a format consumable by downstream applications and services. The system may apply various machine learning operations on the data input including a descriptive analytics analysis, a predictive learning analysis, and/or a prescriptive intelligence analysis.
    Type: Grant
    Filed: February 4, 2019
    Date of Patent: January 30, 2024
    Assignee: American Express Travel Related Services Company, Inc.
    Inventors: Rares Ioan Almasan, Rebecca L. Henry, Rahul Menon
  • Patent number: 11886967
    Abstract: The present invention provides a long-term streamflow forecast method and system based on process-data synergic drive.
    Type: Grant
    Filed: July 17, 2023
    Date of Patent: January 30, 2024
    Assignee: WUHAN UNIVERSITY
    Inventors: Jie Chen, Wenxin Xu, Jiabo Yin, Lihua Xiong, Hua Chen
  • Patent number: 11868857
    Abstract: A video augmentation apparatus is shown. The apparatus may comprise at least a processor and a memory. The processor may be configured to receive a plurality of videos, Additionally, the processor may generate a segment datum as a function of the plurality of videos. The segment datum may be classified to an augmentation datum. The classification may include training an augmentation classifier using a segment training data wherein the segment training data contains a plurality of data entries correlating required segment datum as an input to the augmentation datum as outputs. The classification may further include generating an augmentation classification datum, wherein augmentation classification datum is generated by classifying the segment datum to the augmentation datum using the augmentation classifier. The processor then may generate an augmented video as a function of the augmentation classification datum and display the augmented video using a user display device.
    Type: Grant
    Filed: April 27, 2022
    Date of Patent: January 9, 2024
    Inventor: Eric Strand
  • Patent number: 11853908
    Abstract: Noisy labeled and unlabeled datapoint detection and rectification in a training dataset for machine-learning is facilitated by a processor(s) obtaining a training dataset for use in training a machine-learning model. The processor(s) applies ensemble machine-learning and a generative model to the training dataset to detect noisy labeled datapoints in the training dataset, and create a clean dataset with preliminary labels added for any unlabeled datapoints in the training dataset. Data-driven active learning and the clean dataset are used by the processor(s) to facilitate generating an active-learned dataset with true labels added for one or more selected datapoints of a datapoint pool including the detected noisy labeled datapoints and the unlabeled datapoints of the training dataset. The machine-learning model is trained by the processor(s) using, at least in part, the clean dataset and the active-learned dataset.
    Type: Grant
    Filed: May 13, 2020
    Date of Patent: December 26, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Shaikh Shahriar Quader, Mona Nashaat Ali Elmowafy, Darrell Christopher Reimer
  • Patent number: 11836589
    Abstract: Systems and methods for optimizing trained ML hardware models by collecting machine learning (ML) training inputs and outputs; selecting a ML model architecture from ML model architectures; training the selected ML model architecture with the ML training inputs and outputs; selecting a hardware processor from hardware processors; and creating a trained ML hardware model by inputting the selected hardware processor with the trained ML model. ML test inputs and outputs, and types of test metrics are selected and used to test the trained ML hardware model to provide runtime test metrics data for ML output predictions made by the trained ML hardware model. The trained ML hardware model is optimized to become an optimized trained ML hardware model using the runtime test metrics by selecting a new selected ML model architecture, selecting a new selected hardware processor, or updating the trained ML model using the runtime metrics test data.
    Type: Grant
    Filed: July 11, 2023
    Date of Patent: December 5, 2023
    Assignee: Eta Compute, Inc.
    Inventors: Justin Ormont, Evan Petridis, Luan Nguyen, Jeremi Wojcicki
  • Patent number: 11829891
    Abstract: An integrated hospital logistics management system and an integrated hospital logistics management method using same is provided. Artificial intelligence analyzes trends and seasonal trends by using big data and predicts actual usage by using an artificial intelligence technology, and an artificial intelligence system automatically processes reorders, replacements, etc., thereby ensuring that an appropriate safety stock level can be maintained at all times according to a stock quantity, stock state, issue quantity, etc. of hospital supplies. The integrated hospital logistics management system includes an order processing module which processes ordering and warehousing of supplies; a logistics management module which requests the order processing module to purchase or replace the supplies according to the states of the supplies; and a system control module including a machine learning unit which generates and learns rules about the operation of the supplies by using metadata.
    Type: Grant
    Filed: March 16, 2023
    Date of Patent: November 28, 2023
    Assignee: TBO HEALTHCARE CO., LTD.
    Inventor: Jae Hoon Choi
  • Patent number: 11823073
    Abstract: Provided are systems and methods for auto-completing debriefing processing for a machine learning model pipeline based on a type of predictive algorithm. In one example, the method may include one or more of building a machine learning model pipeline via a user interface, detecting, via the user interface, a selection associated with a predictive algorithm included within the machine learning model pipeline, in response to the selection, identifying debriefing components for the predictive algorithm based on a type of the predictive algorithm from among a plurality of types of predictive algorithms, and automatically incorporating processing for the debriefing components within the machine learning model pipeline such that values of the debriefing components are generated during training of the predictive algorithm within the machine learning model pipeline.
    Type: Grant
    Filed: November 14, 2018
    Date of Patent: November 21, 2023
    Assignee: SAP SE
    Inventor: Jacques Doan Huu