Patents by Inventor Linxia Liao
Linxia Liao 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: 11922310Abstract: Certain aspects of the present disclosure provide techniques for predicting activity within a software application using a machine learning model. An example method generally includes generating a multidimensional time-series data set from time-series data associated with activity within a software application. The multidimensional time-series data set generally includes the time-series data organized based on a plurality of time granularities. Using a machine learning model and the generated multidimensional time-series data set, activity within the software application is predicted for one or more time granularities of the plurality of time granularities. Computing resources are allocated to execute operations using the software application based on the predicted activity within the software application.Type: GrantFiled: March 31, 2023Date of Patent: March 5, 2024Assignee: Intuit, Inc.Inventors: Bor-Chau Juang, Eyal Shafran, Pratyush Kumar Panda, Divya Beeram, Linxia Liao, Nicholas Johnson, Christiana Mei Hui Chen
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Patent number: 11908023Abstract: Certain aspects of the present disclosure provide techniques for generating a user interface to prompt users of a software application to perform an action in the software application. The method generally includes generating historical transaction time gap data for transactions in the account. A probability distribution is generated based on the historical time gap data. The probability distribution represents a probability that a transaction related to the account has been performed after an elapsed time from a previous transaction. A probability that an unrecorded transaction exists for an account based on the probability distribution and a time difference between a most recent transaction and a current time. The probability that an unrecorded transaction exists is determined to exceed a threshold probability, and a user interface is generated and displayed to a user of the software application including a prompt for the user to enter new transactions for the account.Type: GrantFiled: July 29, 2019Date of Patent: February 20, 2024Assignee: Intuit, Inc.Inventors: Meng Chen, Lei Pei, Yueyue Gu, Zhicheng Xue, Linxia Liao
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Patent number: 11574315Abstract: A method and system identify assistance offerings that are likely to be relevant to users of a data management system. The method and system utilize a multivariate random forest regression machine learning process to train an assistance offerings recommendation model to recommend relevant assistance offerings to users of the data management system. The multivariate random forest regression machine learning process replaces zero values in the training set data with negative numbers to increase the accuracy of the machine learning process.Type: GrantFiled: December 17, 2020Date of Patent: February 7, 2023Assignee: Intuit Inc.Inventors: Linxia Liao, Ngoc Nhung Ho, Bei Huang, Meng Chen
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Patent number: 11425250Abstract: Systems and methods for call handling optimization of a call center are disclosed. A system is configured to: obtain a plurality of agent demand variables; obtain an indication of available agents that are associated with a plurality of skills; generate a multi-skill routing matrix depicting a routing problem from the agent demand variables and the available agents; deconstruct the multi-skill matrix into one or more band routing matrices; and identify, for each band routing matrix, a number of agents from the available agents to be used in solving the band routing matrix. Identifying the number of agents includes recursively simulating a plurality of possible routing solutions, measuring a quality metric for each simulation, and reducing the range of available agents based on the quality metric. The system may indicate the number of agents to be used for each routing sub-problem (which may be at the staff group level).Type: GrantFiled: December 6, 2021Date of Patent: August 23, 2022Assignee: Intuit Inc.Inventors: Joseph Brian Cessna, Yaxian Li, Linxia Liao, Kenneth Grant Yocum, Nicholas R. Johnson, Christiana Mei Hui Chen
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Patent number: 11237539Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.Type: GrantFiled: August 7, 2020Date of Patent: February 1, 2022Assignee: PALO ALTO RESEARCH CENTER INCORPORATEDInventors: Linxia Liao, Rajinderjeet Singh Minhas, Arvind Rangarajan, Tolga Kurtoglu, Johan de Kleer
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Publication number: 20210149671Abstract: A machine learning method. A source domain data structure and a target domain data structure are combined into a unified data structure. First data in the source domain data structure are latent with respect to second data in the target domain data structure. The unified data structure includes user vectors that combine the first data and the second data. The user vectors are transformed into a transformed data structure by applying a mapping function to the user vectors. The mapping function relates, using at least one parameter, first relationships in the source domain data structure to second relationships in the target domain data structure. The at least one parameter is based on a combination of affinity scores relating items with which the user interacted and did not interact. The transformed data structure is input into a machine learning model, from which is obtained a recommendation relating to the target domain.Type: ApplicationFiled: November 19, 2019Publication date: May 20, 2021Applicant: Intuit Inc.Inventors: Oren Sar Shalom, Meng Chen, Linxia Liao, Yehezkel Shraga Resheff
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Patent number: 10977663Abstract: A method and system identify assistance offerings that are likely to be relevant to users of a data management system. The method and system utilize a multivariate random forest regression machine learning process to train an assistance offerings recommendation model to recommend relevant assistance offerings to users of the data management system. The multivariate random forest regression machine learning process replaces zero values in the training set data with negative numbers to increase the accuracy of the machine learning process.Type: GrantFiled: February 26, 2018Date of Patent: April 13, 2021Assignee: Intuit Inc.Inventors: Linxia Liao, Ngoc Nhung Ho, Bei Huang, Meng Chen
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Publication number: 20210103935Abstract: A method and system identify assistance offerings that are likely to be relevant to users of a data management system. The method and system utilize a multivariate random forest regression machine learning process to train an assistance offerings recommendation model to recommend relevant assistance offerings to users of the data management system. The multivariate random forest regression machine learning process replaces zero values in the training set data with negative numbers to increase the accuracy of the machine learning process.Type: ApplicationFiled: December 17, 2020Publication date: April 8, 2021Applicant: Intuit Inc.Inventors: Linxia Liao, Ngoc Nhung Ho, Bei Huang, Meng Chen
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Publication number: 20200370996Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.Type: ApplicationFiled: August 7, 2020Publication date: November 26, 2020Inventors: Linxia Liao, Rajinderjeet Singh Minhas, Arvind Rangarajan, Tolga Kurtoglu, Johan de Kleer
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Patent number: 10739230Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.Type: GrantFiled: November 26, 2018Date of Patent: August 11, 2020Assignee: PALO ALTO RESEARCH CENTER INCORPORATEDInventors: Linxia Liao, Rajinderjeet Singh Minhas, Arvind Rangarajan, Tolga Kurtoglu, Johan de Kleer
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Patent number: 10387768Abstract: Embodiments of the present invention provide an enhanced Restricted Boltzmann Machine (RBM) system with a novel regularization term to generate features automatically that are suitable for predicting remaining useful life (RUL) of engineered systems such as machines, tools, apparatus, or parts. The system improves the trendability of the output features, which may better represent the degradation pattern of such systems. The disclosed system has been demonstrated to improve trendability and RUL prediction accuracy, offering improved predictive power earlier in the life cycle of the machine, tool, or part. During operation, the system implements an RBM including a loss function. The system then extracts a set of features from a degradation measurement via the RBM. The system fits a rate-of-change slope for a respective feature and adds a regularization term to the loss function based on the fitted slope.Type: GrantFiled: August 9, 2016Date of Patent: August 20, 2019Assignee: PALO ALTO RESEARCH CENTER INCORPORATEDInventors: Linxia Liao, Wenjing Jin
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Publication number: 20190094108Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.Type: ApplicationFiled: November 26, 2018Publication date: March 28, 2019Inventors: Linxia Liao, Rajinderjeet Singh Minhas, Arvind Rangarajan, Tolga Kurtoglu, Johan de Kleer
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Patent number: 10146611Abstract: A method for controlling a system including a plurality of subsystems, includes receiving operational data from the plurality of subsystems of the system (S21). A future condition of each of the plurality of subsystems is estimated from the received operational data (S22). A control strategy for delaying a need for system maintenance is generated based on the estimated future condition of each of the plurality of subsystems (S23). An operation of the system is controlled based on the generated control strategy (S24).Type: GrantFiled: November 13, 2013Date of Patent: December 4, 2018Assignee: Siemens CorporationInventors: Linxia Liao, Kun Ji
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Patent number: 10139311Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.Type: GrantFiled: September 26, 2014Date of Patent: November 27, 2018Assignee: Palo Alto Research Center IncorporatedInventors: Linxia Liao, Rajinderjeet Singh Minhas, Arvind Rangarajan, Tolga Kurtoglu, Johan de Kleer
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Patent number: 10078062Abstract: A method and system for detecting fault in a machine. During operation, the system obtains control signals and corresponding sensor data that indicates a condition of the machine. The system determines consistent time intervals for each of the control signals. During a consistent time interval the standard deviation of a respective control signal is less than a respective predetermined threshold. The system aggregates the consistent time intervals to determine aggregate consistent intervals. The system then maps the aggregate consistent intervals to the sensor data to determine time interval segments for the sensor data. The system may generate features based on the sensor data. Each respective feature is generated from a time interval segment of the sensor data. The system trains a classifier using the features, and applies the classifier to additional sensor data indicating a condition of the machine over a period of time to detect a machine fault.Type: GrantFiled: December 15, 2015Date of Patent: September 18, 2018Assignee: PALO ALTO RESEARCH CENTER INCORPORATEDInventors: Hoda M. A. Eldardiry, Linxia Liao, Tomonori Honda, Bhaskar Saha, Rui Abreu
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Patent number: 9996405Abstract: A prognostics analysis software module is embedded in a programmable logic controller (PLC) software platform. During cycling of the PLC real-time operating program, data is read from sensors and written to a buffer only when the prognostics analysis software module is idle. The prognostics analysis software module is then activated by a system function block of the PLC software platform. Before determining any prognostic information, prediction models within the prognostics analysis software module are automatically trained using features extracted from the sensor data.Type: GrantFiled: April 12, 2013Date of Patent: June 12, 2018Assignee: Siemens CorporationInventors: Linxia Liao, Ertan Eligul, Zachery Edmondson
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Publication number: 20180046902Abstract: Embodiments of the present invention provide an enhanced Restricted Boltzmann Machine (RBM) system with a novel regularization term to generate features automatically that are suitable for predicting remaining useful life (RUL) of engineered systems such as machines, tools, apparatus, or parts. The system improves the trendability of the output features, which may better represent the degradation pattern of such systems. The disclosed system has been demonstrated to improve trendability and RUL prediction accuracy, offering improved predictive power earlier in the life cycle of the machine, tool, or part. During operation, the system implements an RBM including a loss function. The system then extracts a set of features from a degradation measurement via the RBM. The system fits a rate-of-change slope for a respective feature and adds a regularization term to the loss function based on the fitted slope.Type: ApplicationFiled: August 9, 2016Publication date: February 15, 2018Applicant: Palo Alto Research Center IncorporatedInventors: Linxia Liao, Wenjing Jin
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Publication number: 20180005151Abstract: A system, medium, and method including receiving sequential data relating to one or more assets, the sequential data including state information of the one or more assets over a period of time; determining at least one dependency in the sequential data; optimizing parameters of the sequential data of the one or more assets; and generating, by a survival model, an indicator of a health assessment for the one or more assets.Type: ApplicationFiled: June 29, 2016Publication date: January 4, 2018Inventors: Linxia LIAO, Hyung-il AHN
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Publication number: 20170167993Abstract: A method and system for detecting fault in a machine. During operation, the system obtains control signals and corresponding sensor data that indicates a condition of the machine. The system determines consistent time intervals for each of the control signals. During a consistent time interval the standard deviation of a respective control signal is less than a respective predetermined threshold. The system aggregates the consistent time intervals to determine aggregate consistent intervals. The system then maps the aggregate consistent intervals to the sensor data to determine time interval segments for the sensor data. The system may generate features based on the sensor data. Each respective feature is generated from a time interval segment of the sensor data. The system trains a classifier using the features, and applies the classifier to additional sensor data indicating a condition of the machine over a period of time to detect a machine fault.Type: ApplicationFiled: December 15, 2015Publication date: June 15, 2017Applicant: Palo Alto Research Center IncorporatedInventors: Hoda M. A. Eldardiry, Linxia Liao, Tomonori Honda, Bhaskar Saha, Rui Abreu
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Publication number: 20160091393Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.Type: ApplicationFiled: September 26, 2014Publication date: March 31, 2016Inventors: Linxia Liao, Rajinderjeet Singh Minhas, Arvind Rangarajan, Tolga Kurtoglu, Johan de Kleer