Patents by Inventor Michael LIEBSON
Michael LIEBSON 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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Publication number: 20220405572Abstract: Systems, methods, and software can be used for securing in-tunnel messages. One example of a method includes obtaining a parsed file that comprises two or more sub-feature trees, and each of the two or more sub-feature trees comprise at least one feature layer that comprises features. The method further includes generating a feature vector that identifies the features in the at least one feature layer for each of the two or more sub-feature trees. The method yet further includes mapping the features in the at least one feature layer for each of the one or more sub-feature trees to a corresponding position in the feature vector. By converting features in the parsed file into a feature vector, the method provides an applicable format of the feature vector in wide applications for the parsed file.Type: ApplicationFiled: June 17, 2021Publication date: December 22, 2022Inventors: Yaroslav OLIINYK, David Neill BEVERIDGE, David Michael LIEBSON, Lichun Lily JIA, Eric Glen PETERSEN
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Patent number: 11436520Abstract: Systems and methods are provided herein for redaction of artificial intelligence (AI) training documents. Data comprising an unredacted document is received. The unredacted document comprises a plurality of objects arranged according to a first topology. The unredacted document is parsed to identify objects either directly or relationally containing user sensitive information using a predetermined rule set based on the first topology. The user sensitive information within the unredacted document is substituted with placeholder information to generate a redacted document having a second topology. The second topology is substantially identical to the first topology. In some variations, the redacted document is provided to an AI model for training.Type: GrantFiled: March 7, 2017Date of Patent: September 6, 2022Assignee: Cylance Inc.Inventors: David Neill Beveridge, Yaroslav Oliinyk, David Michael Liebson
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Patent number: 11430244Abstract: A method and computing device for statistical data fingerprinting and tracing data similarity of documents. The method comprises applying a statistical function to a subset of text in a first document thereby generating a first fingerprint; applying the statistical function to a subset of text in a second document thereby generating a second fingerprint; comparing the first fingerprint to the second fingerprint; and determining that the subset of text in the first document matches the subset of text in the second document based on the first fingerprint threshold matching the second fingerprint, wherein the statistical function is a measure of randomness of a count of each character in a subset of text against an expected distribution of said characters.Type: GrantFiled: December 23, 2020Date of Patent: August 30, 2022Assignee: Cylance Inc.Inventors: David Neill Beveridge, David Michael Liebson, Yaroslav Oliinyk
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Publication number: 20220198189Abstract: A method and computing device for statistical data fingerprinting and tracing data similarity of documents. The method comprises applying a statistical function to a subset of text in a first document thereby generating a first fingerprint; applying the statistical function to a subset of text in a second document thereby generating a second fingerprint; comparing the first fingerprint to the second fingerprint; and determining that the subset of text in the first document matches the subset of text in the second document based on the first fingerprint threshold matching the second fingerprint, wherein the statistical function is a measure of randomness of a count of each character in a subset of text against an expected distribution of said characters.Type: ApplicationFiled: December 23, 2020Publication date: June 23, 2022Inventors: David Neill BEVERIDGE, David Michael LIEBSON, Yaroslav OLIINYK
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Patent number: 10181107Abstract: A method, system, and computer program product for generating forecasts and replenishment plans. Some embodiments commence upon receiving point-of-sale data, then receiving distribution-level order data in a second data format. The first point-of-sale data comprises an item identifier and a first date or first date range, and the distribution-level order data comprises the item identifier and a second date or second date range. The originators of the order data are determined using address identifiers (e.g., network location identifiers). The received data is combined wherein at least a portion of the point-of-sale data is combined with at least a portion of the distribution-level order data to generate a combined forecast for the item. Further processing includes receiving an inventory model parameter and combining at least a portion of the first point-of-sale consumption data with at least a portion of the distribution-level order data to generate a replenishment plan for the item.Type: GrantFiled: June 25, 2014Date of Patent: January 15, 2019Assignee: Oracle International CorporationInventors: Nithin Gopinath, Kiran Saindane, Nadav Zivelin, Bart Feldman, Michael Liebson, Vikash Goyal, Nagappan Periakaruppan, Eytan E. Arkin
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Publication number: 20180260734Abstract: Systems and methods are provided herein for redaction of artificial intelligence (AI) training documents. Data comprising an unredacted document is received. The unredacted document comprises a plurality of objects arranged according to a first topology. The unredacted document is parsed to identify objects either directly or relationally containing user sensitive information using a predetermined rule set based on the first topology. The user sensitive information within the unredacted document is substituted with placeholder information to generate a redacted document having a second topology. The second topology is substantially identical to the first topology. In some variations, the redacted document is provided to an AI model for training.Type: ApplicationFiled: March 7, 2017Publication date: September 13, 2018Inventors: David Neill Beveridge, Yaroslav Oliinyk, David Michael Liebson
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Patent number: 10002364Abstract: A method, system, and computer program product for generating forecasts and replenishment plans. Some embodiments commence upon receiving point-of-sale data, then receiving distribution-level order data in a second data format. The first point-of-sale data comprises an item identifier and a first date or first date range, and the distribution-level order data comprises the item identifier and a second date or second date range. The originators of the order data are determined using address identifiers (e.g., network location identifiers). The received data is combined wherein at least a portion of the point-of-sale data is combined with at least a portion of the distribution-level order data to generate a combined forecast for the item. Further processing includes receiving an inventory model parameter and combining at least a portion of the first point-of-sale consumption data with at least a portion of the distribution-level order data to generate a replenishment plan for the item.Type: GrantFiled: June 25, 2014Date of Patent: June 19, 2018Assignee: Oracle International CorporationInventors: Nithin Gopinath, Kiran Saindane, Nadav Zivelin, Bart Feldman, Michael Liebson, Vikash Goyal, Nagappan Periakaruppan, Eytan E. Arkin
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Publication number: 20150379449Abstract: A method, system, and computer program product for generating forecasts and replenishment plans. Some embodiments commence upon receiving point-of-sale data, then receiving distribution-level order data in a second data format. The first point-of-sale data comprises an item identifier and a first date or first date range, and the distribution-level order data comprises the item identifier and a second date or second date range. The originators of the order data are determined using address identifiers (e.g., network location identifiers). The received data is combined wherein at least a portion of the point-of-sale data is combined with at least a portion of the distribution-level order data to generate a combined forecast for the item. Further processing includes receiving an inventory model parameter and combining at least a portion of the first point-of-sale consumption data with at least a portion of the distribution-level order data to generate a replenishment plan for the item.Type: ApplicationFiled: June 25, 2014Publication date: December 31, 2015Applicant: Oracle International CorporationInventors: Nithin GOPINATH, Kiran SAINDANE, Nadav ZIVELIN, Bart FELDMAN, Michael LIEBSON, Vikash GOYAL, Nagappan PERIAKARUPPAN, Eytan E. ARKIN
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Publication number: 20150379536Abstract: A method, system, and computer program product for generating forecasts and replenishment plans. Some embodiments commence upon receiving point-of-sale data, then receiving distribution-level order data in a second data format. The first point-of-sale data comprises an item identifier and a first date or first date range, and the distribution-level order data comprises the item identifier and a second date or second date range. The originators of the order data are determined using address identifiers (e.g., network location identifiers). The received data is combined wherein at least a portion of the point-of-sale data is combined with at least a portion of the distribution-level order data to generate a combined forecast for the item. Further processing includes receiving an inventory model parameter and combining at least a portion of the first point-of-sale consumption data with at least a portion of the distribution-level order data to generate a replenishment plan for the item.Type: ApplicationFiled: June 25, 2014Publication date: December 31, 2015Applicant: Oracle International CorporationInventors: Nithin GOPINATH, Kiran SAINDANE, Nadav ZIVELIN, Bart FELDMAN, Michael LIEBSON, Vikash GOYAL, Nagappan PERIAKARUPPAN, Eytan E. ARKIN