公开(公告)号：US12260306B2

公开(公告)日：2025-03-25

申请号：US17891350

申请日：2022-08-19

Applicant: Oracle International Corporation

Inventor： Kenyu Kobayashi ,  Arno Schneuwly ,  Renata Khasanova ,  Matteo Casserini ,  Felix Schmidt

IPC: G06F8/41 ,  G06N20/00

Abstract: Herein is a machine learning (ML) explainability (MLX) approach in which a natural language explanation is generated based on analysis of a parse tree such as for a suspicious database query or web browser JavaScript. In an embodiment, a computer selects, based on a respective relevance score for each non-leaf node in a parse tree of a statement, a relevant subset of non-leaf nodes. The non-leaf nodes are grouped in the parse tree into groups that represent respective portions of the statement. Based on a relevant subset of the groups that contain at least one non-leaf node in the relevant subset of non-leaf nodes, a natural language explanation of why the statement is anomalous is generated.

公开(公告)号：US12020131B2

公开(公告)日：2024-06-25

申请号：US17221212

申请日：2021-04-02

Applicant: Oracle International Corporation

Inventor： Saeid Allahdadian ,  Amin Suzani ,  Milos Vasic ,  Matteo Casserini ,  Andrew Brownsword ,  Felix Schmidt ,  Nipun Agarwal

IPC: G06N20/20 ,  G06N3/04 ,  G06N3/0442 ,  G06N3/045 ,  G06N3/0495 ,  G06N3/08 ,  G06N3/088 ,  G06N20/00

CPC classification number: G06N20/20 ,  G06N3/04 ,  G06N3/0495 ,  G06N3/08 ,  G06N3/088 ,  G06N3/0442 ,  G06N3/045 ,  G06N20/00

Abstract: Techniques are provided for sparse ensembling of unsupervised machine learning models. In an embodiment, the proposed architecture is composed of multiple unsupervised machine learning models that each produce a score as output and a gating network that analyzes the inputs and outputs of the unsupervised machine learning models to select an optimal ensemble of unsupervised machine learning models. The gating network is trained to choose a minimal number of the multiple unsupervised machine learning models whose scores are combined to create a final score that matches or closely resembles a final score that is computed using all the scores of the multiple unsupervised machine learning models.

公开(公告)号：US11704386B2

公开(公告)日：2023-07-18

申请号：US17199563

申请日：2021-03-12

Applicant: Oracle International Corporation

Inventor： Amin Suzani ,  Saeid Allahdadian ,  Milos Vasic ,  Matteo Casserini ,  Hamed Ahmadi ,  Felix Schmidt ,  Andrew Brownsword ,  Nipun Agarwal

IPC: G06F18/214 ,  G06N20/00 ,  G06V10/75 ,  G06F18/23

CPC classification number: G06F18/214 ,  G06F18/23 ,  G06N20/00 ,  G06V10/758

Abstract: Herein are feature extraction mechanisms that receive parsed log messages as inputs and transform them into numerical feature vectors for machine learning models (MLMs). In an embodiment, a computer extracts fields from a log message. Each field specifies a name, a text value, and a type. For each field, a field transformer for the field is dynamically selected based the field's name and/or the field's type. The field transformer converts the field's text value into a value of the field's type. A feature encoder for the value of the field's type is dynamically selected based on the field's type and/or a range of the field's values that occur in a training corpus of an MLM. From the feature encoder, an encoding of the value of the field's typed is stored into a feature vector. Based on the MLM and the feature vector, the log message is detected as anomalous.

公开(公告)号：US20220188694A1

公开(公告)日：2022-06-16

申请号：US17122401

申请日：2020-12-15

Applicant: Oracle International Corporation

Inventor： Amin Suzani ,  Matteo Casserini ,  Milos Vasic ,  Saeid Allahdadian ,  Andrew Brownsword ,  Hamed Ahmadi ,  Felix Schmidt ,  Nipun Agarwal

IPC: G06N20/00 ,  G06N7/00 ,  G06F17/18

Abstract: Approaches herein relate to model decay of an anomaly detector due to concept drift. Herein are machine learning techniques for dynamically self-tuning an anomaly score threshold. In an embodiment in a production environment, a computer receives an item in a stream of items. A machine learning (ML) model hosted by the computer infers by calculation an anomaly score for the item. Whether the item is anomalous or not is decided based on the anomaly score and an adaptive anomaly threshold that dynamically fluctuates. A moving standard deviation of anomaly scores is adjusted based on a moving average of anomaly scores. The moving average of anomaly scores is then adjusted based on the anomaly score. The adaptive anomaly threshold is then adjusted based on the moving average of anomaly scores and the moving standard deviation of anomaly scores.

公开(公告)号：US20250021759A1

公开(公告)日：2025-01-16

申请号：US18219763

申请日：2023-07-10

Applicant: Oracle International Corporation

Inventor： Samuele Meta ,  Aneesh Dahiya ,  Felix Schmidt ,  Marija Nikolic ,  Matteo Casserini ,  Milos Vasic

IPC: G06F40/284 ,  G06F11/34

Abstract: Herein is natural language processing (NLP) to detect an anomalous log entry using a language model that infers an encoding of the log entry from novel generation of numeric lexical tokens. In an embodiment, a computer extracts an original numeric lexical token from a variable sized log entry. Substitute numeric lexical token(s) that represent the original numeric lexical token are generated, such as with a numeric exponent or by trigonometry. The log entry does not contain the substitute numeric lexical token. A novel sequence of lexical tokens that represents the log entry and contains the substitute numeric lexical token is generated. The novel sequence of lexical tokens does not contain the original numeric lexical token. The computer hosts and operates a machine learning model that generates, based on the novel sequence of lexical tokens that represents the log entry, an inference that characterizes the log entry with unprecedented accuracy.

公开(公告)号：US20240370429A1

公开(公告)日：2024-11-07

申请号：US18143776

申请日：2023-05-05

Applicant: Oracle International Corporation

Inventor： Aneesh Dahiya ,  Matteo Casserini ,  Marija Nikolic ,  Milos Vasic ,  Samuele Meta ,  Nikola Milojkovic ,  Felix Schmidt

IPC: G06F16/2452 ,  G06N3/0455 ,  G06N3/08

Abstract: In an embodiment, a computer generates sentence fingerprints that represent respective pluralities of similar database statements. Based on the sentence fingerprints, an artificial neural network is trained. After training the artificial neural network on a large corpus of fingerprinted database statements, the artificial neural network is ready to be used for zero-shot transfer learning to a downstream task in training. Database statement fingerprinting also anonymizes literal values in raw SQL statements. The trained artificial neural network can be safely reused without risk of disclosing sensitive data in the artificial neural network's vocabulary. After training, the artificial neural network infers a fixed-size encoded database statement from a new database statement. Based on the fixed-size encoded database statement, the new database statement is detected as anomalous, which increases database security and preserves database throughput by not executing the anomalous database statement.

公开(公告)号：US20240345815A1

公开(公告)日：2024-10-17

申请号：US18202564

申请日：2023-05-26

Applicant: Oracle International Corporation

Inventor： Pritam Dash ,  Arno Schneuwly ,  Saeid Allahdadian ,  Matteo Casserini ,  Felix Schmidt

IPC: G06F8/41

CPC classification number: G06F8/427

Abstract: In an embodiment, a computer stores and operates a logic encoder that is an artificial neural network that infers a fixed-size encoded logic from textual or tokenized source logic. Without machine learning, a special parser generates a parse tree that represents the source logic and a fixed-size correctly encoded tree that represents the parse tree. For finetuning the logic encoder, an encoded tree generator is an artificial neural network that accepts the fixed-size encoded logic as input and responsively infers a fixed-size incorrectly encoded tree that represents the parse tree. The neural weights of the logic encoder (and optionally of the encoded tree generator) are adjusted based on backpropagation of error (i.e. loss) as a numerically measured difference between the fixed-size incorrectly encoded tree and the fixed-size correctly encoded tree.

公开(公告)号：US20240037372A1

公开(公告)日：2024-02-01

申请号：US17873491

申请日：2022-07-26

Applicant: Oracle International Corporation

Inventor： Kenyu Kobayashi ,  Arno Schneuwly ,  Renata Khasanova ,  Matteo Casserini ,  Felix Felix Schmidt

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/0454 ,  G06N3/088 ,  G06N3/084

Abstract: The present invention relates to machine learning (ML) explainability (MLX). Herein are techniques for a novel relevance propagation rule in layer-wise relevance propagation (LRP) for feature attribution-based explanation (ABX) for a reconstructive autoencoder. In an embodiment, a reconstruction layer of a reconstructive neural network in a computer generates a reconstructed tuple that is based on an original tuple that contains many features. A reconstruction residual cost function calculates a reconstruction error that measures a difference between the original tuple and the reconstructed tuple. Applied to the reconstruction error is a novel reconstruction relevance propagation rule that assigns a respective reconstruction relevance to each reconstruction neuron in the reconstruction layer. Based on the reconstruction relevance of the reconstruction neurons, a respective feature relevance of each feature is determined, from which an ABX explanation may be automatically generated.

公开(公告)号：US20230334343A1

公开(公告)日：2023-10-19

申请号：US17719617

申请日：2022-04-13

Applicant: Oracle International Corporation

Inventor： Renata Khasanova ,  Nikola Milojkovic ,  Matteo Casserini ,  Felix Schmidt

IPC: G06N5/04

CPC classification number: G06N5/04

Abstract: In an embodiment, a computer hosts a machine learning (ML) model that infers a particular inference for a particular tuple that is based on many features. The features are grouped into predefined super-features that each contain a disjoint (i.e. nonintersecting, mutually exclusive) subset of features. For each super-feature, the computer: a) randomly selects many permuted values from original values of the super-feature in original tuples, b) generates permuted tuples that are based on the particular tuple and a respective permuted value, and c) causes the ML model to infer a respective permuted inference for each permuted tuple. A surrogate model is trained based on the permuted inferences. For each super-feature, a respective importance of the super-feature is calculated based on the surrogate model. Super-feature importances may be used to rank super-features by influence and/or generate a local ML explainability (MLX) explanation.

公开(公告)号：US20250165852A1

公开(公告)日：2025-05-22

申请号：US18514391

申请日：2023-11-20

Applicant: Oracle International Corporation

Inventor： Tomas Feith ,  Arno Schneuwly ,  Saeid Allahdadian ,  Matteo Casserini ,  Felix Schmidt

IPC: G06N20/00

Abstract: During pretraining, a computer generates three untrained machine learning models that are a token sequence encoder, a token predictor, and a decoder that infers a frequency distribution of graph traversal paths. A sequence of lexical tokens is generated that represents a lexical text in a training corpus. A graph is generated that represents the lexical text. In the graph, multiple traversal paths are selected that collectively represent a sliding subsequence of the sequence of lexical tokens. From the subsequence, the token sequence encoder infers an encoded sequence that represents the subsequence of the sequence of lexical tokens. The decoder and token predictor accept the encoded sequence as input for respective inferencing for which respective training losses are measured. Both training losses are combined into a combined loss that is used to increase the accuracy of the three machine learning models by, for example, backpropagation of the combined loss.