Patents Assigned to AGBLOX, INC.
  • Sentiment and rules-based equity analysis using customized neural networks in multi-layer, machine learning-based model
    Patent number: 11893641
    Abstract: A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated.
    Type: Grant
    Filed: April 26, 2021
    Date of Patent: February 6, 2024
    Assignee: AGBLOX, INC.
    Inventors: Thomas N. Blair, Alex A. Kurzhanskiy, Spyros J. Lazaris, Leo Richard Jolicoeur, Michael G. Mcerlean, Tony Chiyung Lei, Craig I. Forman
  • Curated sentiment analysis in multi-layer, machine learning-based forecasting model using customized, commodity-specific neural networks
    Patent number: 10991048
    Abstract: A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated.
    Type: Grant
    Filed: December 28, 2020
    Date of Patent: April 27, 2021
    Assignee: AGBLOX, INC.
    Inventors: Thomas N. Blair, Alex A. Kurzhanskiy, Spyros J. Lazaris, Leo Richard Jolicoeur, Michael G. McErlean, Tony Chiyung Lei, Craig I. Forman
  • Curated sentiment analysis in multi-layer, machine learning-based forecasting model using customized, commodity-specific neural networks
    Patent number: 10878505
    Abstract: A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated.
    Type: Grant
    Filed: July 31, 2020
    Date of Patent: December 29, 2020
    Assignee: AGBLOX, INC.
    Inventors: Thomas N. Blair, Alex A. Kurzhanskiy, Spyros J. Lazaris, Leo Richard Jolicoeur, Michael G. McErlean, Tony Chiyung Lei, Craig I. Forman