Sap Se

Vendavo, Inc.

Sap Ag

Simon Lee

Lauren McMullen

Anubhav Bhatia

Patrick Brose

Lukas Carullo

Leonard Brzezinski

Kun Bao

Karthik Mohan Mokashi

Harpreet Singh

Rashmi Shetty B

Andreas Vogel

Sri Raghupathy

Rafael A Gonzalez-Caloni

Hannes L Marais

Alan Southall

Marc Brown

Michael J Albanese

Tuan Hung Pham

Martin Weiss

Manojit Sarkar

Narayanan Vijaykumar

Niel Crawford Esary

Rashmi B Shetty

Michael Haft

Zhe Pu

Shankar Sundaram

Christine Preisach

Ramesh Seshadri

Shannon Y Zhang

Sean M Murphy

Vinay K Muniganti

Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

Orchestrator for machine learning pipeline

Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Failure mode analytics

Some embodiments provide a program that retrieves a set of notifications describing failures that occurred on a set of monitored devices. The program further determines a set of topics based on the set of notifications. The program also determines failure modes associated with the set of topic from a plurality of failure modes defined for the set of monitored devices. The program further determines failure modes associated with the set of notifications based on the set of topics and the failure modes associated with the set of topics. The program also receives a particular notification that includes a particular set of words describing a failure that occurred on a particular monitored device in the set of monitored devices. The program further determines a failure mode associated with the particular notification based on the particular set of words and the determined failure modes associated with the set of notifications.

Determining failure modes of devices based on text analysis

Provided is a system and method for predicting leading indicators for predicting occurrence of an event at a target asset. Rather than rely on traditional manufacturer-defined leading indicators for an asset, the examples herein predict leading indicators for a target asset based on actual operating conditions at the target asset. Accordingly, unanticipated operating conditions can be considered. In one example, the method may include receiving operating data of a target resource, the operating data being associated with previous occurrences of an event at the target resource, predicting one or more leading indicators of the event at the target resource based on the received operating data, each leading indicator comprising a variable and a threshold value for the variable, and outputting information about the one or more predicted leading indicators of the target resource for display via a user interface.

Predicting leading indicators of an event

In an embodiment, a method of providing an arranged display of data associated with a set of time periods is presented. In this method, values of a first data type are accessed, the values being observed during each of multiple time periods. An order for the time periods is determined based on the values of the first data type. A selectable region for each of the time periods is displayed, the regions being arranged according to the order. In response to a user selection of one of the selectable regions, a value of a second data type is displayed, the value of the second data type being observed during the time period of the selected one of the selectable regions.

Arranged display of data associated with a set of time periods

A benchmarking computing application is configured to create a benchmark run for a segment of customer profiles. Customer profiles from the segment include a parameter, termed an adjustment parameter, that may affect energy consumption by the associated customer. The results of the benchmark run include energy consumption values for individual customer profiles in the segment, an average energy consumption value for the entire segment of customer profiles, and also adjustment energy consumption value. The adjustment energy consumption value reflects the portion of energy consumption by a particular customer attributed to the difference between the average value of the adjustment parameter for the segment and the value of the adjustment parameter for the particular customer profile.

Energy benchmarking analytics

The present invention provides a flexible pricing method for providing pricing adjustments for a product in a deal in response to price variations in selected indexes. The method comprises: designating an index for the product wherein said index has a published index value; and computing a price for said product based on said published index value. The invention also provides for periodically re-pricing deals with index-based pricing terms.

Systems and methods for index-based pricing in a price management system

The present invention provides a system suitable for displaying price modeling data having an indexing module that calculates indexes based on price modeling data and a display module configured display a calculated index. Other embodiments provide a data aggregation module that aggregates indexes calculated by the indexing module that may be also be displayed.

System and method for generating and displaying indexed price modeling data

The present invention presents systems and methods of future-pricing in an integrated price management system. The invention provides a method of maintaining at least one deal with a lowest price status for at least one product in the price management system by inputting a proposed price for product; searching the price management system for deals having the product with the lowest price status; defining a current lowest price as equal to the price of the product; and, if said proposed price is less than said current lowest price, then computing a potential revenue impact of lowering the price of said product to the proposed price.

Integrated price management systems with future-pricing and methods therefor

Pricing decisions and policies in a price management system are made based on a unified database populated with correlated commitment data and transaction data. A historical database is populated with commitment data and transaction data. Each transaction data is then correlated with a commitment data. The resulting correlated data is then analyzed to make pricing decisions and policies.

Methods and systems for making pricing decisions in a price management system

Growing community of inventors

Castro Valley, CA, United States of America

Simon Lee