Patents by Inventor Paulo Abelha Ferreira

Paulo Abelha Ferreira 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: 20210241110
    Abstract: Dynamic adapting neural networks. A latency of a neural network, such as time to inference, is controlled by dynamically compressing/decompressing the neural network. The level of compression or the compression ratio is based on a relationship between the latency and the desired service level. The compression ratio and thus the level of compression can be adjusted until the latency complies with a required latency. A minimum level of accuracy is maintained such that catastrophic forgetting does not occur in the neural network.
    Type: Application
    Filed: January 30, 2020
    Publication date: August 5, 2021
    Inventors: Tiago Salviano Calmon, Vinicius Michel Gottin, Paulo Abelha Ferreira
  • Publication number: 20210232968
    Abstract: An autoregressor that compresses input data for a specific purpose. Input data is compressed using a compression/decompression framework and by accounting for a purpose of a prediction model. The compression aspect of the framework is distributed and the decompression aspect of the framework may be centralized. The compression/decompression framework and a machine learning prediction model can be centrally trained. The compressor is distributed to nodes such that the input data can be compressed and transmitted to a central node. The model and the compression/decompression framework are continually trained on new data. This allows for lossy compression and higher compression rates while maintaining low prediction error rates.
    Type: Application
    Filed: January 29, 2020
    Publication date: July 29, 2021
    Inventors: Paulo Abelha Ferreira, Pablo Nascimento da Silva, Adriana Bechara Prado
  • Publication number: 20210223982
    Abstract: One or more aspects of the present disclosure relate to providing storage system configuration recommendations. System configurations of one or more storage devices can be determined based on their respective collected telemetry information. Performance of storage devices having different system configurations can be predicted based on one or more of: the collected telemetry information and each of the different system configurations. In response to receiving one or more requested performance characteristics and workload conditions, one or more recommended storage device configurations can be provided for each request based on the predicted performance characteristics, the requested performance characteristics, and the workload conditions.
    Type: Application
    Filed: January 17, 2020
    Publication date: July 22, 2021
    Applicant: EMC IP Holding Company LLC
    Inventors: Adriana Bechara Prado, Pablo Nascimento Da Silva, Paulo Abelha Ferreira
  • Publication number: 20210223963
    Abstract: A distribution of response times of a storage system can be estimated for a proposed workload using a trained learning process. Collections of information about operational characteristics of multiple storage systems are obtained, in which each collection includes parameters describing the configuration of the storage system that was used to create the collection, workload characteristics describing features of the workload that the storage system processed, and storage system response times. For each collection, workload characteristics are aggregated, and the storage system response information is used to train a probabilistic mixture model. The aggregated workload information, storage system characteristics, and probabilistic mixture model parameters of the collections form training examples that are used to train the learning process.
    Type: Application
    Filed: January 20, 2020
    Publication date: July 22, 2021
    Inventors: Paulo Abelha Ferreira, Adriana Bechara Prado, Pablo Nascimento da Silva