Patents by Inventor Dieter Gawlick
Dieter Gawlick 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: 20240265308Abstract: Systems, methods, and other embodiments associated with auditing the results of a machine learning model are described. In one embodiment, a method accesses original time series data and machine learning estimates of the original time series data. The method generates reconstituted time series data from the machine learning estimates by reversing operations of a machine learning model trained for generating the machine learning estimates from the original time series data. The method detects tampering (or corruption) in the original time series data based on a difference between the original time series data and reconstituted time series data. And, the method generates an electronic verification report that indicates whether the tampering (or corruption) is detected in the original time series data.Type: ApplicationFiled: March 25, 2024Publication date: August 8, 2024Inventors: Edward R. WETHERBEE, Kenneth P. BACLAWSKI, Guang C. WANG, Kenny C. GROSS, Anna MORAV, Dieter GAWLICK, Zhen Hua LIU, Richard Paul SONDEREGGER
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Publication number: 20240256959Abstract: Systems, methods, and other embodiments associated with detecting unfairness in machine learning outcomes are described. In one embodiment, a method includes generating outcomes for transactions with a machine learning tool to be tested for bias. Then, actual values for a test subset of the outcomes that is associated with a test value for a demographic classification are compared with estimated values for the test subset of outcomes. The estimated values are generated by a machine learning model that is trained with a reference subset of the outcomes that are associated with a reference value for the demographic classification. The method then detects whether the machine learning tool is biased or unbiased based on dissimilarity between the actual values and the estimated values for the test subset of the outcomes. The method then generates an electronic alert that the ML tool is biased or unbiased.Type: ApplicationFiled: July 26, 2023Publication date: August 1, 2024Inventors: Keyang RU, Kenneth P. BACLAWSKI, Richard P. SONDEREGGER, Dieter GAWLICK, Anna CHYSTIAKOVA, Guang Chao WANG, Matthew T. GERDES, Kenny C. GROSS
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Patent number: 12007759Abstract: Techniques for geometric aging data reduction for machine learning applications are disclosed. In some embodiments, an artificial-intelligence powered system receives a first time-series dataset that tracks at least one metric value over time. The system then generates a second time-series dataset that includes a reduced version of a first portion of the time-series dataset and a non-reduced version of a second portion of the time-series dataset. The second portion of the time-series dataset may include metric values that are more recent than the first portion of the time-series dataset. The system further trains a machine learning model using the second time-series dataset that includes the reduced version of the first portion of the time-series dataset and the non-reduced version of the second portion of the time-series dataset. The trained model may be applied to reduced and/or non-reduced data to detect multivariate anomalies and/or provide other analytic insights.Type: GrantFiled: June 28, 2021Date of Patent: June 11, 2024Assignee: Oracle International CorporationInventors: Dieter Gawlick, Matthew Torin Gerdes, Kirk Bradley, Anna Chystiakova, Zhen Hua Liu, Guang Chao Wang, Kenny C. Gross
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Publication number: 20240126741Abstract: A Lock-Free Reservation mechanism is provided. When a transaction issues an update that affects a value in a “reservable column” of a row, the database server does not immediately obtain a lock that covers the row. Instead, the database server adds a reservation to a reservation journal. At the time the transaction commits, a lock is obtained and the requested update is made. In one implementation, before adding the reservation to the reservation journal, the database server determines whether making the update would violate any constraints involving the reservable column. In one implementation, the constraint check not only takes into account the current value of the data item that is being updated and the amount of the update, but also pre-existing reservations in the reservation journal that affect the same data item.Type: ApplicationFiled: July 19, 2023Publication date: April 18, 2024Inventors: VASUDHA KRISHNASWAMY, DIETER GAWLICK, TIRTHANKAR LAHIRI, WEIWEI GONG
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Publication number: 20240126745Abstract: A database-native Lock-Free Reservation infrastructure is used to provide automatic compensation for the reservable column updates made by successful local transactions (or microservice actions) that are part of a saga that is being aborted. The automatic compensation is achieved by tracking the reservable column updates in a reservations journal, within the database, during the execution of the local transaction and remembering them beyond the commit of the local transaction until the finalization of the saga that the transaction is a part of. If the saga aborts, then the database server automatically uses the information retained in the reservations journal to compensate for the changes made by the committed transactions that were part of the saga.Type: ApplicationFiled: July 19, 2023Publication date: April 18, 2024Inventors: VASUDHA KRISHNASWAMY, DIETER GAWLICK, MAHESH BABURAO GIRKAR, AMIT KETKAR, JIAQI WANG, PAVAS NAVANEY
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Patent number: 11948051Abstract: In one embodiment, a method for auditing the results of a machine learning model includes: retrieving a set of state estimates for original time series data values from a database under audit; reversing the state estimation computation for each of the state estimates to produce reconstituted time series data values for each of the state estimates; retrieving the original time series data values from the database under audit; comparing the original time series data values pairwise with the reconstituted time series data values to determine whether the original time series and reconstituted time series match; and generating a signal that the database under audit (i) has not been modified where the original time series and reconstituted time series match, and (ii) has been modified where the original time series and reconstituted time series do not match.Type: GrantFiled: March 23, 2020Date of Patent: April 2, 2024Assignee: ORACLE INTERNATIONAL CORPORATIONInventors: Edward R. Wetherbee, Kenneth P. Baclawski, Guang C. Wang, Kenny C. Gross, Anna Chystiakova, Dieter Gawlick, Zhen Hua Liu, Richard Paul Sonderegger
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Publication number: 20230376837Abstract: Systems, methods, and other embodiments associated with associated with dependency checking for machine learning (ML) models are described. In one embodiment, a method includes applying a repeating probe signal to an input signal input into a machine learning model. An estimate signal output from the machine learning model is monitored, and the repeating probe signal is checked for in the estimate signal. Based on the results of the checking for the repeating probe signal, an evaluation of dependency in the machine learning model is presented.Type: ApplicationFiled: May 23, 2022Publication date: November 23, 2023Inventors: Matthew T. GERDES, Kenneth P. BACLAWSKI, Dieter GAWLICK, Kenny C. GROSS, Guang Chao WANG, Anna CHYSTIAKOVA, Richard P. SONDEREGGER, Zhen Hua LIU
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Patent number: 11797882Abstract: We describe a system that performs prognostic-surveillance operations based on an inferential model that dynamically adapts to evolving operational characteristics of a monitored asset. During a surveillance mode, the system receives a set of time-series signals gathered from sensors in the monitored asset. Next, the system uses an inferential model to generate estimated values for the set of time-series signals, and then performs a pairwise differencing operation between actual values and the estimated values for the set of time-series signals to produce residuals. Next, the system performs a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms. When a tripping frequency of the SPRT alarms exceeds a threshold value, which is indicative of an incipient anomaly in the monitored asset, the system triggers an alert. While the prognostic-surveillance system is operating in the surveillance mode, the system incrementally updates the inferential model based on the time-series signals.Type: GrantFiled: November 21, 2019Date of Patent: October 24, 2023Assignee: Oracle International CorporationInventors: Kenneth P. Baclawski, Dieter Gawlick, Kenny C. Gross, Zhen Hua Liu
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Patent number: 11782429Abstract: The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.Type: GrantFiled: July 7, 2021Date of Patent: October 10, 2023Assignee: Oracle International CorporationInventors: Richard P. Sonderegger, Kenneth P. Baclawski, Guang C. Wang, Anna Chystiakova, Dieter Gawlick, Zhen Hua Liu, Kenny C. Gross
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Patent number: 11775873Abstract: First, the system obtains time-series sensor data. Next, the system identifies missing values in the time-series sensor data, and fills in the missing values through interpolation. The system then divides the time-series sensor data into a training set and an estimation set. Next, the system trains an inferential model on the training set, and uses the inferential model to replace interpolated values in the estimation set with inferential estimates. If there exist interpolated values in the training set, the system switches the training and estimation sets. The system trains a new inferential model on the new training set, and uses the new inferential model to replace interpolated values in the new estimation set with inferential estimates. The system then switches back the training and estimation sets. Finally, the system combines the training and estimation sets to produce preprocessed time-series sensor data, wherein missing values are filled in with imputed values.Type: GrantFiled: June 11, 2018Date of Patent: October 3, 2023Assignee: Oracle International CorporationInventors: Guang C. Wang, Kenny C. Gross, Dieter Gawlick
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Publication number: 20230153680Abstract: Techniques for using machine learning model validated sensor data to generate recommendations for remediating issues in a monitored system are disclosed. A machine learning model is trained to identify correlations among sensors for a monitored system. Upon receiving current sensor data, the machine learning model identifies a subset of the current sensor data that cannot be validated. The system generates estimated values for the sensor data that cannot be validated based on the learned correlations among the sensor values. The system generates the recommendations for remediating the issues in the monitored system based on validated sensor values and the estimated sensor values.Type: ApplicationFiled: November 18, 2021Publication date: May 18, 2023Applicant: Oracle International CorporationInventors: James Charles Rohrkemper, Kenneth Paul Baclawski, Dieter Gawlick, Kenny C. Gross, Guang Chao Wang, Anna Chystiakova, Richard Paul Sonderegger, Zhen Hua Liu
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Publication number: 20230061280Abstract: Techniques for identifying a root cause of an operational result of a deterministic machine learning model are disclosed. A system applies a deterministic machine learning model to a set of data to generate an operational result, such as a prediction of a “fault” or “no-fault” in the system. The set of data includes signals from multiple different data sources, such as sensors. The system applies an abductive model, generated based on the deterministic machine learning model, to the operational result. The abductive model identifies a particular set of data sources that is associated with the root cause of the operational result. The system generates a human-understandable explanation for the operational result based on the identified root cause.Type: ApplicationFiled: August 31, 2021Publication date: March 2, 2023Applicant: Oracle International CorporationInventors: James Charles Rohrkemper, Richard Paul Sonderegger, Anna Chystiakova, Kenneth Paul Baclawski, Dieter Gawlick, Kenny C. Gross, Zhen Hua Liu, Guang Chao Wang
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Publication number: 20230035541Abstract: The disclosed embodiments relate to a system that optimizes a prognostic-surveillance system to achieve a user-selectable functional objective. During operation, the system allows a user to select a functional objective to be optimized from a set of functional objectives for the prognostic-surveillance system. Next, the system optimizes the selected functional objective by performing Monte Carlo simulations, which vary operational parameters for the prognostic-surveillance system while the prognostic-surveillance system operates on synthesized signals, to determine optimal values for the operational parameters that optimize the selected functional objective.Type: ApplicationFiled: July 28, 2021Publication date: February 2, 2023Applicant: Oracle International CorporationInventors: Menglin Liu, Richard P. Sonderegger, Kenneth P. Baclawski, Dieter Gawlick, Anna Chystiakova, Guang C. Wang, Zhen Hua Liu, Hariharan Balasubramanian, Kenny C. Gross
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Publication number: 20230008658Abstract: The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.Type: ApplicationFiled: July 7, 2021Publication date: January 12, 2023Applicant: Oracle International CorporationInventors: Richard P. Sonderegger, Kenneth P. Baclawski, Guang C. Wang, Anna Chystiakova, Dieter Gawlick, Zhen Hua Liu, Kenny C. Gross
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Publication number: 20220413481Abstract: Techniques for geometric aging data reduction for machine learning applications are disclosed. In some embodiments, an artificial-intelligence powered system receives a first time-series dataset that tracks at least one metric value over time. The system then generates a second time-series dataset that includes a reduced version of a first portion of the time-series dataset and a non-reduced version of a second portion of the time-series dataset. The second portion of the time-series dataset may include metric values that are more recent than the first portion of the time-series dataset. The system further trains a machine learning model using the second time-series dataset that includes the reduced version of the first portion of the time-series dataset and the non-reduced version of the second portion of the time-series dataset. The trained model may be applied to reduced and/or non-reduced data to detect multivariate anomalies and/or provide other analytic insights.Type: ApplicationFiled: June 28, 2021Publication date: December 29, 2022Applicant: Oracle International CorporationInventors: Dieter Gawlick, Matthew Torin Gerdes, Kirk Bradley, Anna Chystiakova, Zhen Hua Liu, Guang Chao Wang, Kenny C. Gross
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Patent number: 11468098Abstract: Embodiments of the invention provide systems and methods for managing and processing large amounts of complex and high-velocity data by capturing and extracting high-value data from low value data using big data and related technologies. Illustrative database systems described herein may collect and process data while extracting or generating high-value data. The high-value data may be handled by databases providing functions such as multi-temporality, provenance, flashback, and registered queries. In some examples, computing models and system may be implemented to combine knowledge and process management aspects with the near real-time data processing frameworks in a data-driven situation aware computing system.Type: GrantFiled: June 30, 2020Date of Patent: October 11, 2022Assignee: ORACLE INTERNATIONAL CORPORATIONInventors: Eric S. Chan, Dieter Gawlick, Adel Ghoneimy, Zhen Hua Liu
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Publication number: 20220237509Abstract: Techniques for providing decision rationales for machine-learning guided processes are described herein. In some embodiments, the techniques described herein include processing queries for an explanation of an outcome of a set of one or more decisions guided by one or more machine-learning processes with supervision by at least one human operator. Responsive to receiving the query, a system determines, based on a set of one or more rationale data structures, whether the outcome was caused by human operator error or the one or more machine-learning processes. The system then generates a query response indicating whether the outcome was caused by the human operator error or the one or more machine-learning processes.Type: ApplicationFiled: July 19, 2021Publication date: July 28, 2022Applicant: Oracle International CorporationInventors: John Frederick Courtney, Kenneth Paul Baclawski, Dieter Gawlick, Kenny C. Gross, Guang Chao Wang, Anna Chystiakova, Richard Paul Sonderegger, Zhen Hua Liu
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Publication number: 20210295210Abstract: In one embodiment, a method for auditing the results of a machine learning model includes: retrieving a set of state estimates for original time series data values from a database under audit; reversing the state estimation computation for each of the state estimates to produce reconstituted time series data values for each of the state estimates; retrieving the original time series data values from the database under audit; comparing the original time series data values pairwise with the reconstituted time series data values to determine whether the original time series and reconstituted time series match; and generating a signal that the database under audit (i) has not been modified where the original time series and reconstituted time series match, and (ii) has been modified where the original time series and reconstituted time series do not match.Type: ApplicationFiled: March 23, 2020Publication date: September 23, 2021Inventors: Edward R. WETHERBEE, Kenneth P. BACLAWSKI, Guang C. WANG, Kenny C. GROSS, Anna CHYSTIAKOVA, Dieter GAWLICK, Zhen Hua LIU, Richard Paul SONDEREGGER
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Patent number: 11036756Abstract: Techniques related to an in-memory key-value store for a multi-model database are disclosed. In an embodiment, a relational database may be maintained on persistent storage. The relational database may be managed by a database server and may include a database table. The database table may be stored in a persistent format. Key-value records may be generated within volatile memory accessible to the database server by converting data in the database table to a key-value format. The key-value format may be different from and independent of the persistent format. A database statement referencing the database table may be executed based on determining whether to access one or more key-value records in the volatile memory or to access the data in the database table. In response to determining to access the one or more key-value records, the database server may access the one or more key-value records in the volatile memory.Type: GrantFiled: July 22, 2019Date of Patent: June 15, 2021Assignee: Oracle International CorporationInventors: Christoph Bussler, Dieter Gawlick, Weiwei Gong
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Publication number: 20210158202Abstract: We describe a system that performs prognostic-surveillance operations based on an inferential model that dynamically adapts to evolving operational characteristics of a monitored asset. During a surveillance mode, the system receives a set of time-series signals gathered from sensors in the monitored asset. Next, the system uses an inferential model to generate estimated values for the set of time-series signals, and then performs a pairwise differencing operation between actual values and the estimated values for the set of time-series signals to produce residuals. Next, the system performs a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms. When a tripping frequency of the SPRT alarms exceeds a threshold value, which is indicative of an incipient anomaly in the monitored asset, the system triggers an alert. While the prognostic-surveillance system is operating in the surveillance mode, the system incrementally updates the inferential model based on the time-series signals.Type: ApplicationFiled: November 21, 2019Publication date: May 27, 2021Applicant: Oracle International CorporationInventors: Kenneth P. Baclawski, Dieter Gawlick, Kenny C. Gross, Zhen Hua Liu