Patents by Inventor Scott Garcia
Scott Garcia 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: 20240152419Abstract: Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.Type: ApplicationFiled: January 17, 2024Publication date: May 9, 2024Applicant: Capital One Services, LLCInventors: Vannia GONZALEZ MACIAS, Paul CHO, Rahul GUPTA, Scott GARCIA, Adithya RAMANATHAN
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Patent number: 11977536Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.Type: GrantFiled: March 23, 2023Date of Patent: May 7, 2024Assignee: Capital One Services, LLCInventors: Vannia Gonzalez Macias, Scott Garcia, Peter Terrana
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Publication number: 20240115211Abstract: Disclosed are systems and methods for generating graphical displays of analyte data and/or health information. In some implementations, the graphical displays are generating based on a self-referential dataset that are modifiable based on identified portions of the data. The modified graphical displays can indicate features in the analyte data of a host.Type: ApplicationFiled: December 19, 2023Publication date: April 11, 2024Inventors: Esteban CABRERA, JR., Lauren Danielle ARMENTA, Scott M. BELLIVEAU, Jennifer BLACKWELL, Leif N. BOWMAN, Rian DRAEGER, Arturo GARCIA, Timothy Joseph GOLDSMITH, John Michael GRAY, Andrea Jean JACKSON, Apurv Ullas KAMATH, Katherine Yerre KOEHLER, Paul KRAMER, Aditya Sagar MANDAPAKA, Michael Robert MENSINGER, Sumitaka MIKAMI, Gary A. MORRIS, Hemant Mahendra NIRMAL, Paul NOBLE-CAMPBELL, Philip Thomas PUPA, Eli REIHMAN, Peter C. SIMPSON, Brian Christopher SMITH, Atiim Joseph WILEY
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Patent number: 11945808Abstract: Substituted cyclohexyl chemical entities of Formula (I): wherein Ra, G, and Rb have any of the values described herein, and compositions comprising such chemical entities; methods of making them; and their use in a wide range of methods, including metabolic and reaction kinetic studies; detection and imaging techniques; radioactive therapies; modulating and treating disorders mediated by nociceptin activity or dopamine signaling; treating neurological disorders, neurodegenerative diseases, depression, and schizophrenia; enhancing the efficiency of cognitive and motor training; and treating peripheral disorders, including renal, respiratory, gastrointestinal, liver, genitourinary, metabolic, and inflammatory disorders.Type: GrantFiled: June 15, 2022Date of Patent: April 2, 2024Assignee: Dart Neuroscience, LLCInventors: Jillian Basinger Thompson, Brett C. Bookser, Scott Burley, Pablo Garcia-Reynaga, Andrew Hudson, Marco Peters, Benjamin Pratt, Aaron Thompson, Joe Tran, Lino Valdez
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Publication number: 20240101117Abstract: A number of illustrative variations may include a system including brake-to-steer algorithms may achieve lateral control of a vehicle without longitudinal compensation but may also force a vehicle to slow down too rapidly before appropriate lateral movement can be achieved and may deliver an unnatural driving experience for vehicle occupants. A more natural feeling deceleration may be achieved by optimally selecting appropriate transmission shifts to allow for optimal engine speed or electric motor speed and torque based on current vehicle speed thereby reducing undesirably longitudinal disturbance.Type: ApplicationFiled: October 12, 2023Publication date: March 28, 2024Inventors: Joseph A. LaBarbera, Michael S. Wyciechowski, Clinton L. Schumann, Emmanuel Garcia, Scott T. Sanford, Gregory J. Katch
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Systems, methods, and graphical user interfaces for adding effects in augmented reality environments
Patent number: 11941764Abstract: A computer system displays a representation of a field of view of the one or more cameras, including a representation of a portion of a three-dimensional physical environment that is in the field of view of the one or more cameras. The computer system receives a request to add a first virtual effect to the displayed representation of the field of view of the one or more cameras. In response to receiving the request to add the first virtual effect to the displayed representation of the field of view of the one or more cameras and in accordance with a determination that the first virtual effect requires a scan of the physical environment, the computer system initiates a scan of the physical environment to detect one or more features of the physical environment and displays a user interface that indicates a progress of the scan of the physical environment.Type: GrantFiled: April 13, 2022Date of Patent: March 26, 2024Assignee: APPLE INC.Inventors: Andrew L. Harding, James A. Queen, Joseph-Alexander P. Weil, Joanna M. Newman, Ron A. Buencamino, Richard H. Salvador, Fernando Garcia, Austin T. Tamaddon, Omid Khalili, Scott W. Wilson, Thomas H. Smith, III -
Patent number: 11943191Abstract: Techniques for live location sharing are described. A first mobile device and a second mobile device can communicate with one another using an IM program. The first mobile device can receive a user input to share a location of the first mobile device in the IM program. Sharing the location can include causing the second mobile device to display a location of the first mobile device in an IM program user interface on the second mobile device. Duration of sharing the location can be user-configurable. The second mobile device may or may not share a location of the second device for display in the IM program executing on the first mobile device.Type: GrantFiled: August 5, 2019Date of Patent: March 26, 2024Assignee: Apple Inc.Inventors: Roberto Garcia, Eugene M. Bistolas, Justin Wood, Lawrence Yuan Yang, Scott Lopatin, Richard R. Dellinger
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Patent number: 11931188Abstract: Disclosed are systems and methods for generating graphical displays of analyte data and/or health information. In some implementations, the graphical displays are generating based on a self-referential dataset that are modifiable based on identified portions of the data. The modified graphical displays can indicate features in the analyte data of a host.Type: GrantFiled: September 21, 2021Date of Patent: March 19, 2024Assignee: Dexcom, Inc.Inventors: Esteban Cabrera, Jr., Lauren Danielle Armenta, Scott M. Belliveau, Jennifer Blackwell, Leif N. Bowman, Rian Draeger, Arturo Garcia, Timothy Joseph Goldsmith, John Michael Gray, Andrea Jean Jackson, Apurv Ullas Kamath, Katherine Yerre Koehler, Paul Kramer, Aditya Sagar Mandapaka, Michael Robert Mensinger, Sumitaka Mikami, Gary A Morris, Hemant Mahendra Nirmal, Paul Noble-Campbell, Philip Thomas Pupa, Eli Reihman, Peter C. Simpson, Brian Christopher Smith, Atiim Joseph Wiley
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Publication number: 20240071593Abstract: Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.Type: ApplicationFiled: October 24, 2023Publication date: February 29, 2024Inventors: Anna Leigh DAVIS, Scott M. BELLIVEAU, Naresh C. BHAVARAJU, Leif N. BOWMAN, Rita M. CASTILLO, Alexandra Elena CONSTANTIN, Rian W. DRAEGER, Laura J. DUNN, Gary Brian GABLE, Arturo GARCIA, Thomas HALL, Hari HAMPAPURAM, Christopher Robert HANNEMANN, Anna Claire HARLEY-TROCHIMCZYK, Nathaniel David HEINTZMAN, Andrea Jean JACKSON, Lauren Hruby JEPSON, Apurv Ullas KAMATH, Katherine Yerre KOEHLER, Aditya Sagar MANDAPAKA, Samuel Jere MARSH, Gary A. MORRIS, Subrai Girish PAI, Andrew Attila PAL, Nicholas POLYTARIDIS, Philip Thomas PUPA, Eli REIHMAN, Ashley Anne RINDFLEISCH, Sofie Wells SCHUNK, Peter C. SIMPSON, Daniel S. SMITH, Stephen J. VANSLYKE, Matthew T. VOGEL, Tomas C. WALKER, Benjamin Elrod WEST, Atiim Joseph WILEY
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Patent number: 11914462Abstract: Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.Type: GrantFiled: January 10, 2023Date of Patent: February 27, 2024Assignee: Capital One Services, LLCInventors: Vannia Gonzalez Macias, Paul Cho, Rahul Gupta, Scott Garcia, Adithya Ramanathan
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Publication number: 20240004847Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.Type: ApplicationFiled: September 13, 2023Publication date: January 4, 2024Applicant: Capital One Services, LLCInventors: Vannia GONZALEZ MACIAS, Scott GARCIA, Peter TERRANA
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Publication number: 20230259504Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.Type: ApplicationFiled: March 23, 2023Publication date: August 17, 2023Applicant: Capital One Services, LLCInventors: Vannia GONZALEZ MACIAS, Scott GARCIA, Peter TERRANA
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Patent number: 11640387Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.Type: GrantFiled: April 23, 2021Date of Patent: May 2, 2023Assignee: Capital One Services, LLCInventors: Vannia Gonzalez Macias, Scott Garcia, Peter Terrana
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Patent number: 11579958Abstract: Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.Type: GrantFiled: April 23, 2021Date of Patent: February 14, 2023Assignee: Capital One Services, LLCInventors: Vannia Gonzalez Macias, Paul Cho, Rahul Gupta, Scott Garcia, Adithya Ramanathan
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Publication number: 20220342860Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.Type: ApplicationFiled: April 23, 2021Publication date: October 27, 2022Applicant: Capital One Services, LLCInventors: Vannia GONZALEZ MACIAS, Scott Garcia, Peter Terrana
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Publication number: 20220342745Abstract: Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.Type: ApplicationFiled: April 23, 2021Publication date: October 27, 2022Applicant: Capital One Services, LLCInventors: Vannia Gonzalez Macias, Paul Cho, Rahul Gupta, Scott Garcia, Adithya Ramanathan
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Publication number: 20220342868Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.Type: ApplicationFiled: April 23, 2021Publication date: October 27, 2022Applicant: Capital One Services, LLCInventors: Vannia Gonzalez Macias, Scott Garcia, Peter Terrana