Intuit, Inc.

Palo Alto Research Center Inc.

Siemens Corporation

Linxia Liao

Johan De Kleer

Arvind Rangarajan

Rajinderjeet Singh Minhas

Meng Chen

Tolga Kurtoglu

Ngoc Nhung Ho

Bei Huang

Christiana Mei Hui Chen

Nicholas R Johnson

Bhaskar Saha

Joseph B Cessna

Lei Pei

Kun Ji

Hoda M A Eldardiry

Divya Beeram

Zhicheng Xue

Kenneth Grant Yocum

Tomonori Honda

Rui Abreu

Yaxian Li

Zachery Edmondson

Ertan Eligul

Wenjing Jin

Bor-Chau Juang

Yueyue Gu

Pratyush Kumar Panda

Eyal Shafran

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.

Forecasting activity in software applications using machine learning models and multidimensional time-series data

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.

Method and system for generating user interfaces to prompt users to perform an activity in a software application based on transaction time analysis

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.

Method and system for recommending assistance offerings

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).

Artificial intelligence based call handling optimization

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.

System and method for operational-data-based detection of anomaly of a machine tool

Method and system for recommending assistance offerings using multivariate random forest leveraging negative samples

System and method for detecting anomaly of a machine tool

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. The system then selects a subset of the enhanced features based on a measure of monotonic trending and aggregates the subset into a health value. The system then predicts a RUL as a weighted average of features best matching a historical degradation pattern in the health value.

Enhanced restricted boltzmann machine with prognosibility regularization for prognostics and health assessment

A method for controlling a system including a plurality of subsystems, includes receiving operational data from the plurality of subsystems of the system (S). A future condition of each of the plurality of subsystems is estimated from the received operational data (S). 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 (S). An operation of the system is controlled based on the generated control strategy (S).

Resilient optimization and control for distributed systems

Computer-implemented method and system for machine tool damage assessment, prediction, and planning in manufacturing shop floor

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.

Device health estimation by combining contextual information with sensor data

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.

Embedded prognostics on PLC platforms for equipment condition monitoring, diagnosis and time-to-failure/service prediction

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Mountain View, CA, United States of America

Linxia Liao