US 2022/0044133 A1Published application

Detection of anomalous data using machine learning

AssigneeSap SE

InventorsMichael Otto, Min-Ho Hong, Markus Umlauff, Lars Vogelgesang-Moll

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Abstract

Techniques and solutions are described for analyzing data collections to determine if they may be anomalous as compared with other data collections. For example, one or more values for data elements of a data collection may be unusually high or low, or may represent infrequently occurring values. Or, values of data elements in a data collection may not be anomalous when considered individually, but may be anomalous in combination. A machine learning model is trained with training data collections, where the training data collections include a plurality of data elements. An inference data collection, also having the data elements of the training data collections, is analyzed using the trained machine learning model to provide an anomaly score. The anomaly score can be based at least in part on feature anomaly scores, which indicate anomality of individual data elements of the inference data collection.

Claims20

Dependent claims

A computing system comprising:

memory;
one or more processing units coupled to the memory;
and
one or more computer readable storage media storing instructions that, when loaded into the memory and executed by the one or more processing units, cause the one or more processing units to perform operations for:
receiving a request for an anomaly score for an inference data collection;
receiving the inference data collection, the inference data collection comprising a plurality of features, each feature being associated with a data type;
calculating an anomaly score for the inference data collection using a machine learning model, wherein the anomaly score indicates a relative difference between the inference data collection and a plurality of training data collections used to train the machine learning model, the anomaly score representing a combination of feature anomaly scores for at least a portion of the features of the inference data collection, wherein the training data collections comprise the plurality of features;
and
returning an inference result in response to the request, the inference result comprising at least a portion of the feature anomaly scores.

6 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/988,528Filed 7 Aug 2020
Published: 10 Feb 2022
Term ends: Not estimatedThe source records don’t support an estimate.

Classification15 codes

G06N 5/04
Computing arrangements using knowledge-based models: inference or reasoning models
G06N 20/20
Machine learning: ensemble learning
G06F 18/213
Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation: feature extraction, e.g.…
G06F 18/214
Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation: generating training…

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