Palantir Technologies Inc.

Arthur Thouzeau

Spencer Tank

Ankit Shankar

Peter Maag

Thomas Powell

Geoff Stowe

Ezra Spiro

Andre Frederico Cavalheiro Menck

Anshuman Prasad

Joanna Peller

Charles Shepherd

Jennifer Yip

Marco Diciolla

Matthew Todd

Caroline Henry

Bianca Rahill-Marier

Casey Patton

Xinyi Wang

Michal Duczynski

Benedict Cappellacci

Sebastiaan Visser

Issei Nino

Matthew Rauen

Sorin-Alexandru Nutu

Bianca Rahil-Marier

Systems and methods are provided for storing data representing respective sub-elements of a complex task. Data representing one or more links between two or more sub-elements is stored, the links indicating a dependency between said sub-elements. A work order is calculated based on the identified links. A graphical representation of the calculated work order which indicates said sub-elements and their dependencies is provided. The links may indicate a temporal dependency of a second sub-element on a first sub-element and in which the provided graphical representation presents the temporal relationship of the sub-elements. Historical data may be received for association with one or more selected links or sub-elements, the historical data related to a prior event and which affects the temporal relationship between the sub-elements. An updated work order modified by the historical data may be calculated. An updated graphical representation of the work order may be provided.

Graphical representation of a complex task

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

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

Methods, systems, and computer-readable media described herein may include: obtaining first data from a first datastore managed by a first entity, the first data comprising a first status of a first task of a first subsidiary workflow that is part of an integrated workflow; integrating the first data into a second datastore managed by a second entity that manages the integrated workflow, with an integration layer; generating a graphical user interface to present an interactive graphical object that displays the integrated first data and the integrated workflow to a user; receiving one or more updates from the user to the integrated first data, where the one or more updates are based on one or more graphical user interactions with the integrated first data; identifying a change to a portion of the first data; and writing the change in the first datastore using the integration layer interface.

Automating management of integrated workflows based on disparate subsidiary data sources

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. 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.

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London, United Kingdom

Arthur Thouzeau