Palantir Technologies Inc.

Andre Frederico Cavalheiro Menck

Thomas Powell

Ezra Spiro

Peter Maag

Daniel Erenrich

Spencer Tank

Anshuman Prasad

David Lisuk

Guodong Xu

Anirvan Mukherjee

Arthur Thouzeau

Aleksandr Zamoshchin

Simon Slowik

Joanna Peller

Charles Shepherd

Luis Voloch

Daniel Chin

Marco Diciolla

Jennifer Yip

Caroline Henry

Matthew Todd

Rahul Kumar Agarwal

Myles Scolnick

Bianca Rahill-Marier

Steven Fackler

Jim Inoue

William Seaton

Paul Gribelyuk

Maciej Albin

Benjamin Lee

Andrew Poh

Jonah Scheinerman

Benjamin Grabham

Gregoire Omont

Arion Sprague

Tam-Sanh Nguyen

Michael Xiaoxuan Yang

Eliot Ball

James Edward Winchester

Yifei Huang

Matthew Jenny

Andy Chen

Francis Chen

Peter Wang

Christopher Yu

Nitish Kulkarni

Grace Garde

Mark Cinali

Xiangnong Wang

Rahul Agarwal

Systems, methods, and non-transitory computer-readable media can be configured to perform receiving a notification of a maintenance event associated with a resource. The method includes retrieving historic maintenance data in relation to the resource with which the fault is associated, the maintenance information originating from a time period preceding the time of the maintenance event. The method includes identifying at least a portion of the retrieved historic maintenance data as being indicative of the maintenance event. The method also includes causing the portion of the retrieved historic maintenance data identified as being indicative of the maintenance event to be stored as a precursor signal of the maintenance event. The method also includes causing future maintenance data received from a plurality of resources related to the resource with which the maintenance event is associated to be monitored to predict a future occurrence of the maintenance event in the plurality of resources.

Predictive modelling

A model management system provides a centralized repository for storing and accessing models. The model management system receives an input to store a model object in a first model state generated based on a first set of known variables. The model management system generates a first file including a first set of functions defining the first model state and associates the first file with a model key identifying the model object. The model management system receives an input to store the model object in a second model state having been generated based on the first model state and a second set of known variables. The model management system generates a second file including a second set of functions defining the second model state and associates the second file with the model key. The model management system identifies available versions of the model object based on the model key.

Model object management and storage system

Systems and methods are provided for identifying relationships between defects. The system may obtain defect items and associated information. Defect items may be compared to one another based on their attributes to determine how related they are. According to the comparisons, defect items may be grouped together into issue items for further analysis by a user. The system may further update a defect comparison model according to user interaction with defect items.

Systems and methods for determining relationships between defects

Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period; access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs; and determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.

Machine fault modelling

Various systems and methods for aggregating data from disparate sources to determine an optimal package of data items are disclosed. For example, the system described herein can obtain data items from various sources, aggregate and/or organize the data items into an optimal package based on various criteria, and present, via an interactive user interface, the optimal package. Furthermore, the interactive user interface may enable a user to adjust the criteria used to aggregate and/or organize the data items. The system may interactively re-aggregate and re-organize the data items using the adjusted criteria as the user interacts with the package via the user interface. The system and user interface may thus enable the user to optimize the packages of data items based on multiple factors quickly and efficiently.

System and method for efficiently determining and displaying optimal packages of data items

A method of assigning a task to a resource in a multiple resource environment is performed by one or more processors or special-purpose computing hardware. The method includes receiving task information relating to at least one task to be performed by a resource in the multiple resource environment. The method also includes determining a cost value for each task, the cost value indicating a cost incurred if a maintenance event occurs during performance of the respective task. The method also includes receiving predictive maintenance information in relation to each of the multiple resources in the multiple resource environment, the predictive maintenance information indicating a likelihood of a maintenance event with respect to each of the multiple resources in the multiple resource environment. The method also includes allocating the at least one task to one of the resources in the multiple resource environment dependent on the predictive maintenance information of the multiple resources and the calculated cost score.

Task allocation

Systems, methods, non-transitory computer readable media can be configured to accessing a target sensor log corresponding to a first machine; accessing one or more prior sensor logs corresponding to the first machine and one or more prior sensor logs corresponding to a plurality of second machines which are of the same type as the first machine; accessing a plurality of computer readable logs corresponding to the first machine and the second machines, the computer readable logs for each second machine comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determining a set of statistical metrics characterising a difference between the target sensor log and each prior sensor log; selecting a sub-set of the prior sensor logs in dependence upon the statistical metrics; analysing the maintenance logs to correlate each prior sensor log included in the subset to one or more correlated maintenance tasks; selecting a priority maintenance task based on the sub-set of prior sensor logs, t and the correlated maintenance tasks; and outputting the priority maintenance task.

Determining maintenance for a machine

A computer-implemented method comprises receiving one or more data transformation commands through a console in a console session, the one or more data transformation commands relating to one or more initial datasets; executing the one or more data transformation commands using the one or more initial datasets to modify at least one of the one or more initial datasets to generate a modified dataset; generating a set of environment flags for the command to indicate that the one or more initial datasets has been accessed and the at least one dataset that has been modified; and updating a set of line dependencies based on the generated set of environmental flags and previously generated sets of environmental flags for one or more previously executed commands.

Data pipeline creation system and method

A method of processing sensor logs is described. The method includes accessing a first sensor log and a corresponding first reference log. Each of the first sensor log and the first reference log includes a series of measured values of a parameter according to a first time series. The method also includes accessing a second sensor log and a corresponding second reference log. Each of the second sensor log and the second reference log includes a series of measured values of a parameter according to a second time series. The method also includes dynamically time warping the first reference log and/or and second reference log by a first transformation between the first time series and a common time-frame and/or a second transformation between the second time series and the common time-frame. The method also includes generating first and second warped sensor logs by applying the or each transformation to the corresponding ones of the first and second sensor logs.

Processing sensor logs

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New York, NY, United States of America

Andre Frederico Cavalheiro Menck