Patents by Inventor Omar Aguilar Macedo
Omar Aguilar Macedo 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).
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Patent number: 11709798Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating, via a processor, a set of hashes for each of a plurality of objects. The method also comprises computing, via the processor, a high-dimensional sparse vector for each object, where the vector represents the set of hashes for each object. The method further comprises computing, via the processor, a combined high-dimensional sparse vector from the high-dimensional sparse vectors for all objects and computing a hash suppression threshold. The method also comprises determining, via the processor, a group of hashes to be suppressed by using the hash suppression threshold, and suppressing, via the processor, the group of selected hashes when performing an action.Type: GrantFiled: October 13, 2021Date of Patent: July 25, 2023Assignee: Hewlett Packard Enterprise Development LPInventors: Mehran Kafai, Kave Eshghi, Omar Aguilar Macedo
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Publication number: 20220066988Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating, via a processor, a set of hashes for each of a plurality of objects. The method also comprises computing, via the processor, a high-dimensional sparse vector for each object, where the vector represents the set of hashes for each object. The method further comprises computing, via the processor, a combined high-dimensional sparse vector from the high-dimensional sparse vectors for all objects and computing a hash suppression threshold. The method also comprises determining, via the processor, a group of hashes to be suppressed by using the hash suppression threshold, and suppressing, via the processor, the group of selected hashes when performing an action.Type: ApplicationFiled: October 13, 2021Publication date: March 3, 2022Inventors: Mehran Kafai, Kave Eshghi, Omar Aguilar Macedo
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Patent number: 11169964Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating, via a processor, a set of hashes for each of a plurality of objects. The method also comprises computing, via the processor, a high-dimensional sparse vector for each object, where the vector represents the set of hashes for each object. The method further comprises computing, via the processor, a combined high-dimensional sparse vector from the high-dimensional sparse vectors for all objects and computing a hash suppression threshold. The method also comprises determining, via the processor, a group of hashes to be suppressed by using the hash suppression threshold, and suppressing, via the processor, the group of selected hashes when performing an action.Type: GrantFiled: December 11, 2015Date of Patent: November 9, 2021Assignee: Hewlett Packard Enterprise Development LPInventors: Mehran Kafai, Kave Eshghi, Omar Aguilar Macedo
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Publication number: 20200167312Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating, via a processor, a set of hashes for each of a plurality of objects. The method also comprises computing, via the processor, a high-dimensional sparse vector for each object, where the vector represents the set of hashes for each object. The method further comprises computing, via the processor, a combined high-dimensional sparse vector from the high-dimensional sparse vectors for all objects and computing a hash suppression threshold. The method also comprises determining, via the processor, a group of hashes to be suppressed by using the hash suppression threshold, and suppressing, via the processor, the group of selected hashes when performing an action.Type: ApplicationFiled: December 11, 2015Publication date: May 28, 2020Inventors: Mehran Kafai, Kave Eshghi, Omar Aguilar Macedo
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Patent number: 10579623Abstract: Dynamically updating a ridge regression data model of a continuous stream of data is disclosed. New data chunks corresponding to a current data accumulation point are received and the data values in the new data chunks are transformed via standardization methods. A ridge estimator for the standardized data that includes data chunks received up to a penultimate data accumulation point to include the new data chunks is dynamically updated. The cumulative observations received up to the current data accumulation point are updated and stored. Predictions for the continuous data stream are generated based on the updated ridge estimator.Type: GrantFiled: April 29, 2016Date of Patent: March 3, 2020Assignee: Hewlett Packard Enterprise Development LPInventors: Mehran Kafai, Hongwei Shang, Omar Aguilar Macedo
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Patent number: 10362553Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating a base image representing a location of a wireless-enabled device with data from a plurality of wireless beacons and generating an image fingerprint for the location of the wireless-enabled device by using the base image. The method further comprises comparing the image fingerprint for the location of the wireless-enabled device with a plurality of existing image fingerprints associated with the plurality of wireless beacons, and determining a location of the wireless-enabled device based on the comparison.Type: GrantFiled: June 4, 2015Date of Patent: July 23, 2019Assignee: ENTIT SOFTWARE LLCInventors: Mehran Kafai, Le An, Omar Aguilar Macedo
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Publication number: 20180176878Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating a base image representing a location of a wireless-enabled device with data from a plurality of wireless beacons and generating an image fingerprint for the location of the wireless-enabled device by using the base image. The method further comprises comparing the image fingerprint for the location of the wireless-enabled device with a plurality of existing image fingerprints associated with the plurality of wireless beacons, and determining a location of the wireless-enabled device based on the comparison.Type: ApplicationFiled: June 4, 2015Publication date: June 21, 2018Inventors: Mehran Kafai, Le An, Omar Aguilar Macedo
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Publication number: 20170316338Abstract: In some examples, a method includes accessing input vectors in an input space, wherein the input vectors characterize elements of a physical system. The method may also include generating feature vectors from the input vectors, and the feature vectors are generated without any vector product operations between performed between any of the input vectors. An inner product of a pair of the feature vectors may correlate to an implicit kernel for the pair of feature vectors, and the implicit kernel may approximate a Gaussian kernel within a difference threshold. The method may further include providing the feature vectors to an application engine for use in analyzing the elements of the physical system, other elements in the physical system, or a combination of both.Type: ApplicationFiled: April 29, 2016Publication date: November 2, 2017Inventors: Kave Eshghi, Mehran Kafai, Omar Aguilar Macedo
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Publication number: 20170316341Abstract: Dynamically updating a ridge regression data model of a continuous stream of data is disclosed. New data chunks corresponding to a current data accumulation point are received and the data values in the new data chunks are transformed via standardization methods. A ridge estimator for the standardized data that includes data chunks received up to a penultimate data accumulation point to include the new data chunks is dynamically updated. The cumulative observations received up to the current data accumulation point are updated and stored. Predictions for the continuous data stream are generated based on the updated ridge estimator.Type: ApplicationFiled: April 29, 2016Publication date: November 2, 2017Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LPInventors: Mehran KAFAI, Hongwei SHANG, Omar AGUILAR MACEDO