Patents by Inventor Stefano Stefani

Stefano Stefani 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).

  • Patent number: 10387402
    Abstract: A system that implements a scaleable data storage service may maintain tables in a non-relational data store on behalf of clients. Each table may include multiple items. Each item may include one or more attributes, each containing a name-value pair. Attribute values may be scalars or sets of numbers or strings. The system may provide an API usable to request that values of one or more of an item's attributes be updated. An update request may be conditional on expected values of one or more item attributes (e.g., the same or different item attributes). In response to a request to update the values of one or more item attributes, the previous values and/or updated values may be optionally returned for the updated item attributes or for all attributes of an item targeted by an update request. Items stored in tables may be indexed using a simple or composite primary key.
    Type: Grant
    Filed: November 28, 2016
    Date of Patent: August 20, 2019
    Assignee: Amazon Technologies, Inc.
    Inventors: Stefano Stefani, Chiranjeeb Buragohain, Jai Vasanth, Wei Xiao
  • Patent number: 10382255
    Abstract: A system and method for failover in a distributed system may comprise a computing device that receives information associating computing nodes with ordinal identifiers, such that the computing nodes are divided into at least a first and second subset based on the identifiers. The identifiers may further define an ordering of the subsets. When failover occurs, candidate computing nodes may be identified and selected based at least in part on the ordering. Secondary considerations, including functions performed by other members of a candidate's subset, are considered when identifying candidate nodes.
    Type: Grant
    Filed: June 28, 2016
    Date of Patent: August 13, 2019
    Assignee: Amazon Technologies, Inc.
    Inventors: Sandor Loren Maurice, Alexey Kuznetsov, Stefano Stefani
  • Patent number: 10372926
    Abstract: A distributed data store may implement passive distribution encryption keys to enable access to encrypted data stored in the distributed data store. Keys to encrypt a data volume stored in the distributed data store may be encrypted according to a distribution key and provided to a client of the distributed data store. Storage nodes that maintain portions of the data volume may receive the encrypted key from a client to enable access to the data volume. The storage nodes may decrypt the key according to the distribution key and enable access to the data volume at the storage nodes. In to some embodiments, a key hierarchy may be implemented to encrypt the keys that provide access to the encrypted data. The key hierarchy may include a user key.
    Type: Grant
    Filed: December 21, 2015
    Date of Patent: August 6, 2019
    Assignee: Amazon Technologies, Inc.
    Inventors: Yan Valerie Leshinsky, Lon Lundgren, Stefano Stefani
  • Patent number: 10331791
    Abstract: A natural language understanding model is trained using respective natural language example inputs corresponding to a plurality of applications. A determination is made as to whether a value of a first parameter of a first application is to be obtained using a natural language interaction. Using the natural language understanding model, at least a portion of the first application is generated.
    Type: Grant
    Filed: November 23, 2016
    Date of Patent: June 25, 2019
    Assignee: Amazon Technologies, Inc.
    Inventors: Vikram Sathyanarayana Anbazhagan, Rama Krishna Sandeep Pokkunuri, Swaminathan Sivasubramanian, Stefano Stefani, Vladimir Zhukov
  • Patent number: 10310737
    Abstract: Solid-state storage devices may be employed to store data maintained by a database management system, but may have characteristics that reduce the efficiency of interactions between the database management system and the device. A storage subsystem may receive information indicative of internal boundaries within database data. A segment of the database data may be selected for compression, wherein the size of the segment is based at least on one or more the internal boundaries, the memory page size of the solid-state drive, and a predicted rate of compression. The compressed segment may be stored if it has a size less than the memory page size of the device. If it does not, compression may be retried with a smaller segment of data or a portion of the data may be stored in uncompressed form. Additional segments of the data may be stored on the solid-state drive in a similar manner.
    Type: Grant
    Filed: January 12, 2017
    Date of Patent: June 4, 2019
    Assignee: Amazon Technologies, Inc.
    Inventors: Subramanian Sankara Subramanian, Stefano Stefani
  • Patent number: 10311055
    Abstract: A query optimizer may receive a query (e.g., from a source that generated the query). Input that specifies both a query hint string and a hint may be received to a hint specification interface. The hint may be applied to the query, from outside the query, to optimize a query execution plan. Applying the hint may be based, at least in part, on a query hint string. For example, which query block is associated with the query hint string may be determined. Upon such a determination, the hint may be applied to the determined query block.
    Type: Grant
    Filed: May 9, 2016
    Date of Patent: June 4, 2019
    Assignee: Amazon Technologies, Inc.
    Inventors: Neil Thombre, Anurag Windlass Gupta, Stefano Stefani, Aleksandras Surna
  • Publication number: 20190164080
    Abstract: Techniques for auto-scaling hosted machine learning models for production inference are described. A machine learning model can be deployed in a hosted environment such that the infrastructure supporting the machine learning model scales dynamically with demand so that performance is not impacted. The model can be auto-scaled using reactive techniques or predictive techniques.
    Type: Application
    Filed: November 24, 2017
    Publication date: May 30, 2019
    Inventors: Stefano STEFANI, Steven Andrew LOEPPKY, Thomas Albert FAULHABER, JR., Craig WILEY, Edo LIBERTY
  • Publication number: 20190156247
    Abstract: Techniques for dynamic accuracy-based experimentation and deployment of machine learning (ML) models are described. Inference traffic flowing to ML models and the accuracy of the models is analyzed and used to ensure that better performing models are executed more often via model selection. A predictive component can evaluate which model is more likely to be accurate for certain input data elements. Ensemble techniques can combine inference results of multiple ML models to aim to achieve a better overall result than any individual model could on its own.
    Type: Application
    Filed: March 13, 2018
    Publication date: May 23, 2019
    Inventors: Thomas Albert FAULHABER, JR., Edo LIBERTY, Stefano STEFANI, Zohar KARNIN, Craig WILEY, Steven Andrew LOEPPKY, Swaminathan SIVASUBRAMANIAN, Alexander Johannes SMOLA, Taylor GOODHART
  • Publication number: 20190156816
    Abstract: Techniques for automated speech recognition (ASR) are described. A user can upload an audio file to a storage location. The user then provides the ASR service with a reference to the audio file. An ASR engine analyzes the audio file, using an acoustic model to divide the audio data into words, and a language model to identify the words spoken in the audio file. The acoustic model can be trained using audio sentence data, enabling the transcription service to accurately transcribe lengthy audio data. The results are punctuated and normalized, and the resulting transcript is returned to the user.
    Type: Application
    Filed: March 15, 2018
    Publication date: May 23, 2019
    Inventors: Ashish SINGH, Deepikaa SURESH, Vasanth PHILOMIN, Rajkumar GULABANI, Vladimir ZHUKOV, Swaminathan SIVASUBRAMANIAN, Vikram Sathyanarayana ANBAZHAGAN, Praveen Kumar AKARAPU, Stefano STEFANI
  • Publication number: 20190156124
    Abstract: Techniques for analyzing stored video upon a request are described. For example, a method of receiving a first application programming interface (API) request to analyze a stored video, the API request to include a location of the stored video and at least one analysis action to perform on the stored video; accessing the location of the stored video to retrieve the stored video; segmenting the accessed video into chunks; processing each chunk with a chunk processor to perform the at least one analysis action, each chunk processor to utilize at least one machine learning model in performing the at least one analysis action; joining the results of the processing of each chunk to generate a final result; storing the final result; and providing the final result to a requestor in response to a second API request is described.
    Type: Application
    Filed: March 20, 2018
    Publication date: May 23, 2019
    Inventors: Nitin SINGHAL, Vivek BHADAURIA, Ranju DAS, Gaurav D. GHARE, Roman GOLDENBERG, Stephen GOULD, Kuang HAN, Jonathan Andrew HEDLEY, Gowtham JEYABALAN, Vasant MANOHAR, Andrea OLGIATI, Stefano STEFANI, Joseph Patrick TIGHE, Praveen Kumar Udayakumar, Renjun ZHANG
  • Publication number: 20190156244
    Abstract: A network-accessible machine learning service is provided herein. For example, the network-accessible machine learning service provider can operate one or more physical computing devices accessible to user devices via a network. These physical computing device(s) can host virtual machine instances that are configured to train machine learning models using training data referenced by a user device. These physical computing device(s) can further host virtual machine instances that are configured to execute trained machine learning models in response to user-provided inputs, generating outputs that are stored and/or transmitted to user devices via the network.
    Type: Application
    Filed: November 22, 2017
    Publication date: May 23, 2019
    Inventors: Thomas Albert Faulhaber, Jr., Stefano Stefani, Owen Thomas
  • Publication number: 20190132332
    Abstract: A data-collecting device acquires data associated with a real-time data stream and transmits the data to a data-consuming service hosted on a server computer system in the form of a multipart response. The multipart response includes one or more data content parts and at least one authentication content part. Each of the one or more data content parts contains data representing part of the real-time data stream. Each authentication content part includes authentication information usable to verify the integrity of the data transmitted in the data content parts transmitted prior to the authentication content part.
    Type: Application
    Filed: December 26, 2018
    Publication date: May 2, 2019
    Inventors: Ameya Karnik, Stefano Stefani
  • Publication number: 20190079839
    Abstract: Disclosed are various embodiments for distributing data items within a plurality of nodes. A data item that is subject to a data item update request is updated from a master node to a plurality of slave notes. The update of the data item is determined to be locality-based durable based at least in part on acknowledgements received from the slave nodes. Upon detection that the master node has failed, a new master candidate is determined via an election among the plurality of slave nodes.
    Type: Application
    Filed: November 9, 2018
    Publication date: March 14, 2019
    Applicant: Amazon Technologies, Inc.
    Inventors: Michael T. Helmick, Jakub Kulesza, Timothy Andrew Rath, Stefano Stefani, David Alan Lutz
  • Patent number: 10171477
    Abstract: A data-collecting device acquires data associated with a real-time data stream and transmits the data to a data-consuming service hosted on a server computer system in the form of a multipart response. The multipart response includes one or more data content parts and at least one authentication content part. Each of the one or more data content parts contains data representing part of the real-time data stream. Each authentication content part includes authentication information usable to verify the integrity of the data transmitted in the data content parts transmitted prior to the authentication content part.
    Type: Grant
    Filed: February 14, 2017
    Date of Patent: January 1, 2019
    Assignee: Amazon Technologies, Inc.
    Inventors: Ameya Karnik, Stefano Stefani
  • Publication number: 20180366114
    Abstract: Methods, systems, and computer-readable media for exporting dialog-driven applications to digital communication platforms are disclosed. A launch condition is received from a user. The launch condition is caused to be registered with one or more digital communication platforms. Detection of the launch condition is to cause a natural language input to be routed from at least one of the digital communication platforms to an application management service.
    Type: Application
    Filed: June 16, 2017
    Publication date: December 20, 2018
    Applicant: Amazon Technologies, Inc.
    Inventors: Vikram Sathyanarayana Anbazhagan, Swaminathan Sivasubramanian, Stefano Stefani, Vladimir Zhukov
  • Patent number: 10158579
    Abstract: Methods and apparatus for resource silos at network-accessible services are disclosed. A subset of resources used for a database service, including at least one resource from each of a plurality of data centers, is selected for membership in a resource silo based on grouping criteria. A silo routing layer node identifies the resource silo as the target silo to which a client work request is to be directed. The client work request is sent to a front-end resource of the target silo either by the client, or by the silo routing layer node on behalf of the client. The front-end resource of the target silo transmits a representation of the work request to a back-end resource of the target silo, where a work operation corresponding to request is performed.
    Type: Grant
    Filed: June 21, 2013
    Date of Patent: December 18, 2018
    Assignee: Amazon Technologies, Inc.
    Inventors: Adam Douglas Morley, Vincent Anthony Brancato, Stefano Stefani, Jai Vasanth, Wei Xiao, Maximiliano Maccanti, Swaminathan Sivasubramanian, Rande A. Blackman, Timothy Andrew Rath
  • Patent number: 10127123
    Abstract: Disclosed are various embodiments for distributing data items within a plurality of nodes. A data item that is subject to a data item update request is updated from a master node to a plurality of slave notes. The update of the data item is determined to be locality-based durable based at least in part on acknowledgements received from the slave nodes. Upon detection that the master node has failed, a new master candidate is determined via an election among the plurality of slave nodes.
    Type: Grant
    Filed: January 24, 2017
    Date of Patent: November 13, 2018
    Assignee: Amazon Technologies, Inc.
    Inventors: Michael T. Helmick, Jakub Kulesza, Timothy Andrew Rath, Stefano Stefani, David Alan Lutz
  • Publication number: 20180314712
    Abstract: Data to be stored in a data block for a columnar database table may be compressed according to a multi-level compression scheme. Data to be stored in the data block may be received. The data may be compressed according a column-specific compression technique to produce compressed data. The compressed data may then be compressed according to a second compression technique different than the column-specific compression technique to produce multi-level compressed data. The multi-level compressed data may be stored in the data block. When reading from the data block, multi-level compressed data may be decompressed according to the column-specific compression technique and the default compression technique applied to the data.
    Type: Application
    Filed: July 6, 2018
    Publication date: November 1, 2018
    Applicant: Amazon Technologies, Inc.
    Inventors: Stefano Stefani, Anurag Windlass Gupta
  • Publication number: 20180240163
    Abstract: A computer system that provides clients access to pooled resources in order to provide computing or data storage services may receive service requests (which explicitly or implicitly include requests for pooled resources), may service at least some of those requests, and may determine pricing for the serviced requests. The pricing for each request may be dependent on whether it was serviced using a portion of a resource pool that was reserved for the use of the client on whose behalf it was received or using burst capacity (e.g., unreserved or otherwise idle capacity within the resource pool). Pricing for the use of reserved capacity may be fixed, regardless of the amount of reserved capacity used. Pricing for burst capacity may depend on actual use, and may be demand-based (e.g., using a spot-market-based dynamic pricing model). Clients with reserved capacity may optionally request access to burst capacity.
    Type: Application
    Filed: April 20, 2018
    Publication date: August 23, 2018
    Applicant: Amazon Technologies, Inc.
    Inventors: Swaminathan Sivasubramanian, Stefano Stefani
  • Patent number: 10019457
    Abstract: Data to be stored in a data block for a columnar database table may be compressed according to a multi-level compression scheme. Data to be stored in the data block may be received. The data may be compressed according a column-specific compression technique to produce compressed data. The compressed data may then be compressed according to a second compression technique different than the column-specific compression technique to produce multi-level compressed data. The multi-level compressed data may be stored in the data block. When reading from the data block, multi-level compressed data may be decompressed according to the column-specific compression technique and the default compression technique applied to the data.
    Type: Grant
    Filed: January 22, 2013
    Date of Patent: July 10, 2018
    Assignee: Amazon Technologies, Inc.
    Inventors: Stefano Stefani, Anurag Windlass Gupta