Patents by Inventor Nicholas McCarthy

Nicholas McCarthy has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20200356829
    Abstract: The systems and methods described herein may generate multi-modal embeddings with sub-symbolic features and symbolic features. The sub-symbolic embeddings may be generated with computer vision processing. The symbolic features may include mathematical representations of image content, which are enriched with information from background knowledge sources. The system may aggregate the sub-symbolic and symbolic features using aggregation techniques such as concatenation, averaging, summing, and/or maxing. The multi-modal embeddings may be included in a multi-modal embedding model and trained via supervised learning. Once the multi-modal embeddings are trained, the system may generate inferences based on linear algebra operations involving the multi-modal embeddings that are relevant to an inference response to the natural language question and input image.
    Type: Application
    Filed: May 8, 2019
    Publication date: November 12, 2020
    Applicant: Accenture Global Solutions Limited
    Inventors: Luca Costabello, Nicholas McCarthy, Rory McGrath, Sumit Pai
  • Publication number: 20200327963
    Abstract: The disclosure enables latent space exploration of a dataset based on drug molecular-structure data and drug biological-treatment data for a set of drug compounds in order to determine optimal drug compounds for treating diseases. Regional interpolation, including a linear interpolation (LERP) operation and a non-linear interpolation operation such as a spherical linear interpolation (SLERP), along with quantitative structure-activity relationship (QSAR) models may be utilized to navigate through a latent space generated from a variational auto-encoder (VAE), in accordance with certain embodiments.
    Type: Application
    Filed: June 19, 2019
    Publication date: October 15, 2020
    Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITED
    Inventors: Qurrat Ul Ain, Nicholas McCarthy, Jeremiah Hayes, Philip O'Kelly, Patrick Moreau
  • Patent number: 10803055
    Abstract: This disclosure relates to a development and application of a deep-learning neural network (DNN) model for identifying relevance of an information item returned by a search engine in response to a search query by a user, with respect to the search query and a profile for the user. The DNN model includes a set of neural networks arranged to learn correlations between queries, search results, and user profiles using dense numerical word or character embeddings and based on training targets derived from a historical search log containing queries, search results, and user-click data. The DNN model help identifying search results that are relevant to users according to their profiles.
    Type: Grant
    Filed: December 15, 2017
    Date of Patent: October 13, 2020
    Assignee: Accenture Global Solutions Limited
    Inventors: Jadran Sirotkovic, Nicholas McCarthy
  • Publication number: 20200242484
    Abstract: Complex computer system architectures are described for utilizing a knowledge data graph comprised of elements, and selecting a discovery element to replace an existing element of a formulation depicted in the knowledge data graph. The substitution process takes advantage of the knowledge data graph structure to improve the computing capabilities of a computing device executing a substitution calculation by translating the knowledge data graph into an embedding space, and determining a discovery element from within the embedding space.
    Type: Application
    Filed: January 24, 2019
    Publication date: July 30, 2020
    Applicant: Accenture Global Solutions Limited
    Inventors: Freddy Lecue, Chahrazed Bouhini, Jeremiah Hayes, Mykhaylo Zayats, Nicholas McCarthy, Qurrat Ul Ain
  • Publication number: 20200219245
    Abstract: A process receives, with a processor, video content. Further, the process splices, with the processor, the video content into a plurality of video frames. In addition, the process splices, with the processor, at least one of the plurality of video frames into a plurality of image patches. Moreover, the process performs, with a neural network, an image reconstruction of at least one of the plurality of image patches to generate a reconstructed image patch. The process also compares, with the processor, the reconstructed image patch with the at least one of the plurality of image patches. Finally, the process determines, with the processor, a pixel error within the at least one of the plurality of image patches based on a discrepancy between the reconstructed image patch and the at least one of the plurality of image patches.
    Type: Application
    Filed: January 9, 2019
    Publication date: July 9, 2020
    Inventors: Erika Doggett, Anna Wolak, Penelope Daphne Tsatsoulis, Nicholas McCarthy, Stephan Mandt
  • Patent number: 10638668
    Abstract: Implementations include providing a baseline multi-dimensional model of a cultivar, determining an encoding based on the baseline multi-dimensional model, and a target multi-dimensional model, the encoding defining a string of symbols, and being based on an alphabet and a set of rules, providing an expected multi-dimensional model based on the encoding, and a modified set of rules, the modified set of rules being based on the set of rules, the expected multi-dimensional model representing the cultivar after a period of time, selecting a set of actions by determining multiple predicted multi-dimensional models for each set of actions in a plurality of sets of actions, and, for each predicted multi-dimensional model, providing a predicted yield that can be used to determine impact with respect an expected yield, the set of actions being selected based on a respective impact, and providing the set of actions as output for executing on the cultivar.
    Type: Grant
    Filed: August 30, 2018
    Date of Patent: May 5, 2020
    Assignee: Accenture Global Solutions Limited
    Inventors: Christophe Dominique Marie Gueret, Freddy Lecue, Nicholas McCarthy
  • Publication number: 20200110746
    Abstract: Knowledge graph systems are disclosed for enhancing a knowledge graph by generating a new node. The knowledge graph system converts a knowledge graph into an embedding space, and selects a region of interest from within the embedding space. The knowledge graph system further identifies, from the region of interest, one or more gap regions, and calculates a center for each gap region. A node is generated for each gap region, and the information represented by the node is added to the original knowledge graph to generate an updated knowledge graph.
    Type: Application
    Filed: December 18, 2018
    Publication date: April 9, 2020
    Applicant: Accenture Global Solutions Limited
    Inventors: Freddy Lecue, Chahrazed Bouhini, Jeremiah Hayes, Mykhaylo Zayats, Nicholas McCarthy, Qurrat Ul Ain
  • Publication number: 20200068807
    Abstract: Implementations include providing a baseline multi-dimensional model of a cultivar, determining an encoding based on the baseline multi-dimensional model, and a target multi-dimensional model, the encoding defining a string of symbols, and being based on an alphabet and a set of rules, providing an expected multi-dimensional model based on the encoding, and a modified set of rules, the modified set of rules being based on the set of rules, the expected multi-dimensional model representing the cultivar after a period of time, selecting a set of actions by determining multiple predicted multi-dimensional models for each set of actions in a plurality of sets of actions, and, for each predicted multi-dimensional model, providing a predicted yield that can be used to determine impact with respect an expected yield, the set of actions being selected based on a respective impact, and providing the set of actions as output for executing on the cultivar.
    Type: Application
    Filed: August 30, 2018
    Publication date: March 5, 2020
    Inventors: Christophe Dominique Marie Gueret, Freddy Lecue, Nicholas McCarthy
  • Publication number: 20190188295
    Abstract: This disclosure relates to a development and application of a deep-learning neural network (DNN) model for identifying relevance of an information item returned by a search engine in response to a search query by a user, with respect to the search query and a profile for the user. The DNN model includes a set of neural networks arranged to learn correlations between queries, search results, and user profiles using dense numerical word or character embeddings and based on training targets derived from a historical search log containing queries, search results, and user-click data. The DNN model help identifying search results that are relevant to users according to their profiles.
    Type: Application
    Filed: December 15, 2017
    Publication date: June 20, 2019
    Inventors: Jadran Sirotkovic, Nicholas McCarthy
  • Patent number: 10262079
    Abstract: A device may receive individual information associated with individual activities of an individual, and may aggregate the individual information, based on a time period, to generate aggregated individual information. The device may identify patterns in the aggregated individual information, and may determine states for the patterns based on state information associated with activities capable of being performed by individuals. The device may generate a sequential knowledge graph based on modifying a knowledge graph with the states and adding a sequence of activities to the knowledge graph, and may determine embeddings for the individual activities based on the sequential knowledge graph.
    Type: Grant
    Filed: September 25, 2018
    Date of Patent: April 16, 2019
    Assignee: Accenture Global Solutions Limited
    Inventors: Luca Costabello, Christophe Dominique Marie Gueret, Freddy Lecue, Jeremiah Hayes, Nicholas McCarthy