Patents by Inventor Robert Redinger
Robert Redinger 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: 11915802Abstract: The technology disclosed relates to efficient tertiary analysis of genomic data. The technology disclosed includes splitting a genomic data file into a plurality of segments, and storing segments in the plurality of segments across nodes of a distributed storage system, pushing the segments from the nodes of the distributed storage system to nodes of a distributed, in-memory computing engine, distributing directives of tertiary analysis job contexts for the genomic data file across the nodes of the distributed, in-memory computing engine, directly executing the distributed directives on the segments stored on the nodes of the distributed, in-memory computing engine to cause parallel processing of the segments, and aggregating results of the parallel processing across the nodes of the distributed, in-memory computing engine to produce an output.Type: GrantFiled: August 4, 2020Date of Patent: February 27, 2024Assignee: SHARECARE AI, INC.Inventors: Brett Robert Redinger, Kartik Thakore, Sandra Ann R Steyaert, Walter Adolf De Brouwer, Srivatsa Akshay Sharma, Lijing Guo
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Patent number: 11811794Abstract: The technology disclosed provides systems and methods related to preventing exfiltration of training data by feature reconstruction attacks on model instances trained on the training data during a training job. The system comprises a privacy interface that presents a plurality of modulators for a plurality of training parameters. The modulators are configured to respond to selection commands via the privacy interface to trigger procedural calls. The procedural calls modify corresponding training parameters in the plurality of training parameters for respective training cycles in the training job. The system comprises a trainer configured to execute the training cycles in dependence on the modified training parameters. The trainer can determine a performance accuracy of the model instances for each of the executed training cycles.Type: GrantFiled: May 12, 2021Date of Patent: November 7, 2023Assignee: Sharecare AI, Inc.Inventors: Gabriel Gabra Zaccak, William Hartman, Andrés Rodriguez Esmeral, Devin Daniel Reich, Marina Titova, Brett Robert Redinger, Philip Joseph Dow, Satish Srinivasan Bhat, Walter Adolf De Brouwer, Scott Michael Kirk
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Patent number: 11755709Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: GrantFiled: February 21, 2022Date of Patent: September 12, 2023Assignee: SHARECARE AI, INC.Inventors: Axel Sly, Srivatsa Akshay Sharma, Brett Robert Redinger, Devin Daniel Reich, Geert Trooskens, Meelis Lootus, Young Jin Lee, Ricardo Lopez Arredondo, Frederick Franklin Kautz, IV, Satish Srinivasan Bhat, Scott Michael Kirk, Walter Adolf De Brouwer, Kartik Thakore
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Publication number: 20220337418Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic biometric identifiers. The method includes generating a scannable code upon receiving a success nonce from a registration server. The registration server can access a user identifier and a hash of at least a signature using the success nonce. The signature can be generated based at least in part upon a biometric identifier of a user. The method includes recreating the hash of the signature stored by the registration server. The method includes generating the scannable code by encrypting the success nonce and the recreated hash. The biometric identifier of the user is generated by feeding a plurality of non-deterministic biometric inputs to a trained machine learning model producing a plurality of feature vectors. The method includes projecting the plurality of feature vectors onto a surface of a unit hyper-sphere and computing a characteristic identity vector representing the user.Type: ApplicationFiled: November 15, 2021Publication date: October 20, 2022Applicant: Sharecare AI, Inc.Inventors: Axel SLY, Srivatsa Akshay SHARMA, Brett Robert REDINGER, Devin Daniel REICH, Geert TROOSKENS, Meelis LOOTUS, Young Jin LEE, Ricardo Lopez ARREDONDO, Frederick Franklin KAUTZ, IV, Satish Srinivasan BHAT, Scott Michael KIRK, Walter Adolf DE BROUWER, Kartik THAKORE
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Publication number: 20220269771Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: ApplicationFiled: May 2, 2022Publication date: August 25, 2022Applicant: SHARECARE AI, INC.Inventors: Axel SLY, Srivatsa Akshay SHARMA, Brett Robert REDINGER, Devin Daniel REICH, Geert TROOSKENS, Meelis LOOTUS, Young Jin LEE, Ricardo Lopez ARREDONDO, Frederick Franklin KAUTZ, IV, Satish Srinivasan BHAT, Scott Michael KIRK, Walter Adolf DE BROUWER, Kartik THAKORE
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Publication number: 20220179943Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: ApplicationFiled: February 21, 2022Publication date: June 9, 2022Applicant: SHARECARE AI, INC.Inventors: Axel SLY, Srivatsa Akshay SHARMA, Brett Robert REDINGER, Devin Daniel REICH, Geert TROOSKENS, Meelis LOOTUS, Young Jin LEE, Ricardo Lopez ARREDONDO, Frederick Franklin KAUTZ, IV, Satish Srinivasan BHAT, Scott Michael KIRK, Walter Adolf DE BROUWER, Kartik THAKORE
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Patent number: 11321447Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: GrantFiled: April 20, 2021Date of Patent: May 3, 2022Assignee: SHARECARE AI, INC.Inventors: Axel Sly, Srivatsa Akshay Sharma, Brett Robert Redinger, Devin Daniel Reich, Geert Trooskens, Meelis Lootus, Young Jin Lee, Ricardo Lopez Arredondo, Frederick Franklin Kautz, IV, Satish Srinivasan Bhat, Scott Michael Kirk, Walter Adolf De Brouwer, Kartik Thakore
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Patent number: 11256801Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: GrantFiled: April 20, 2021Date of Patent: February 22, 2022Assignee: doc.ai, Inc.Inventors: Axel Sly, Srivatsa Akshay Sharma, Brett Robert Redinger, Devin Daniel Reich, Geert Trooskens, Meelis Lootus, Young Jin Lee, Ricardo Lopez Arredondo, Frederick Franklin Kautz, IV, Satish Srinivasan Bhat, Scott Michael Kirk, Walter Adolf De Brouwer, Kartik Thakore
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Publication number: 20210360010Abstract: The technology disclosed provides systems and methods related to preventing exfiltration of training data by feature reconstruction attacks on model instances trained on the training data during a training job. The system comprises a privacy interface that presents a plurality of modulators for a plurality of training parameters. The modulators are configured to respond to selection commands via the privacy interface to trigger procedural calls. The procedural calls modify corresponding training parameters in the plurality of training parameters for respective training cycles in the training job. The system comprises a trainer configured to execute the training cycles in dependence on the modified training parameters. The trainer can determine a performance accuracy of the model instances for each of the executed training cycles.Type: ApplicationFiled: May 12, 2021Publication date: November 18, 2021Applicant: Sharecare AI, Inc.Inventors: Gabriel Gabra ZACCAK, William HARTMAN, Andrés Rodriguez ESMERAL, Devin Daniel REICH, Marina TITOVA, Brett Robert REDINGER, Philip Joseph DOW, Satish Srinivasan BHAT, Walter Adolf DE BROUWER, Scott Michael KIRK
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Patent number: 11177960Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: GrantFiled: April 20, 2021Date of Patent: November 16, 2021Assignee: Sharecare AI, Inc.Inventors: Axel Sly, Srivatsa Akshay Sharma, Brett Robert Redinger, Devin Daniel Reich, Geert Trooskens, Meelis Lootus, Young Jin Lee, Ricardo Lopez Arredondo, Frederick Franklin Kautz, IV, Satish Srinivasan Bhat, Scott Michael Kirk, Walter Adolf De Brouwer, Kartik Thakore
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Publication number: 20210326422Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: ApplicationFiled: April 20, 2021Publication date: October 21, 2021Applicant: doc.ai, Inc.Inventors: Axel SLY, Srivatsa Akshay SHARMA, Brett Robert REDINGER, Devin Daniel REICH, Geert TROOSKENS, Meelis LOOTUS, Young Jin LEE, Ricardo Lopez ARREDONDO, Frederick Franklin KAUTZ, IV, Satish Srinivasan BHAT, Scott Michael KIRK, Walter Adolf DE BROUWER, Kartik THAKORE
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Publication number: 20210328801Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: ApplicationFiled: April 20, 2021Publication date: October 21, 2021Applicant: doc.ai, Inc.Inventors: Axel SLY, Srivatsa Akshay SHARMA, Brett Robert REDINGER, Devin Daniel REICH, Geert TROOSKENS, Meelis LOOTUS, Young Jin LEE, Ricardo Lopez ARREDONDO, Frederick Franklin KAUTZ, IV, Satish Srinivasan BHAT, Scott Michael KIRK, Walter Adolf DE BROUWER, Kartik THAKORE
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Publication number: 20210326433Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: ApplicationFiled: April 20, 2021Publication date: October 21, 2021Applicant: doc.ai, Inc.Inventors: Axel SLY, Srivatsa Akshay SHARMA, Brett Robert REDINGER, Devin Daniel REICH, Geert TROOSKENS, Meelis LOOTUS, Young Jin LEE, Ricardo Lopez ARREDONDO, Frederick Franklin KAUTZ, IV, Satish Srinivasan BHAT, Scott Michael KIRK, Walter Adolf DE BROUWER, Kartik THAKORE
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Publication number: 20210043285Abstract: The technology disclosed relates to efficient tertiary analysis of genomic data. The technology disclosed includes splitting a genomic data file into a plurality of segments, and storing segments in the plurality of segments across nodes of a distributed storage system, pushing the segments from the nodes of the distributed storage system to nodes of a distributed, in-memory computing engine, distributing directives of tertiary analysis job contexts for the genomic data file across the nodes of the distributed, in-memory computing engine, directly executing the distributed directives on the segments stored on the nodes of the distributed, in-memory computing engine to cause parallel processing of the segments, and aggregating results of the parallel processing across the nodes of the distributed, in-memory computing engine to produce an output.Type: ApplicationFiled: August 4, 2020Publication date: February 11, 2021Applicant: doc.ai, Inc.Inventors: Brett Robert REDINGER, Kartik THAKORE, Sandra Ann R STEYAERT, Walter Adolf DE BROUWER, Srivatsa Akshay SHARMA, Lijing GUO
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Patent number: 4460754Abstract: A process is described for the two-stage mass polymerization of vinyl chloride, in which 0.1 to 3% by weight, relative to the monomer employed, of one or more of the following compounds which are known as heat stabilizers for polyvinyl chloride: monoalkyltin-IV or dialkyltin-IV compounds in which the residual valencies of the tin are attached to organic radicals via sulfur atoms, and/or aminocrotonic acid esters, are added to the polymerization mixture when a conversion of 65 to 85% has been reached. After thorough removal of the unreacted vinyl chloride from the resulting polymer, preferably at temperatures above the glass transition point of the polymer, products are obtained which have an improved color in comparison with polyvinyl chloride produced by conventional mass polymerization.Type: GrantFiled: June 1, 1982Date of Patent: July 17, 1984Assignee: Hoechst AktiengesellschaftInventors: Robert Redinger, Walter Berngruber
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Patent number: 4043731Abstract: In a calender the nip between a pair of rollers, of which one is obliquely adjustable, is bounded at the sides by plates adapted to the geometrical shape of the nip region and provided with slots, into which a control beam, parallel to the obliquely adjustable roller, engages, the ends of the beam are joined to the bearings of this roller, and the plates are arranged on supporting plates so as to be displaceble by means of a guide device, so that the plates, on oblique adjustment of the roller, are moved along a path which is concentric with the surface of the roller which is not obliquely adjustable.Type: GrantFiled: November 16, 1976Date of Patent: August 23, 1977Assignee: Hoechst AktiengesellschaftInventors: Helmut Kratzmann, Josef Heinik, Robert Obermaier, Erich Zentner, Robert Redinger