The Marker Fingerprinting system provides a method for identifying and correlating serial or sequential data patterns across diverse domains such as geological, biological, and financial datasets. This innovation transforms single- or multi-attribute data series into feature matrices, generating unique hash tokensor fingerprintsthat encapsulate specific data patterns. Using advanced signal analysis and spectral transformations, it enables efficient processing and pattern recognition within complex datasets. Fingerprints from reference patterns are matched against target datasets, with quantitative confidence metrics derived from weighted algorithms assessing match accuracy. Iterative data conditioning enhances robustness by addressing noise and inconsistencies, ensuring reliability at scale. The invention improves decision-making by delivering rapid and accurate pattern identification with quantified reliability, making it particularly suited for applications like geological top picking, seismic data analysis, and other fields requiring precise data correlation

A device that includes a master boss and a slave boss. The slave boss is configured to iteratively send execute and output commands to a first plurality of nodes implemented by a node engine identified in a first boss table in response to receiving an execute command from the master boss. The master boss is configured to iteratively send execute and output commands to the slave boss and a second plurality of nodes implemented by the node engine identified in a second boss table. Each node is configured to receive a first correlithm object, fetch a second correlithm object based on the first correlithm object in response to receiving an execute command, and output the second correlithm object in response to receiving an output command.

An Online Machine Learning System (OMLS) includes an Online Machine Learning Engine (OMLE) for incorporating and utilizing one or more machine learning algorithms or models utilizing features to generate a result, and capable of incorporating and utilizing multiple different machine learning algorithms; wherein the OMLS is configured to perform continuous online machine learning, the continuous online machine learning comprising: continuous online machine learning from streaming data including an instance comprising a vector of inputs, the vector of inputs comprising a plurality of continuous or categorical features; and continuous online machine learning from periodically provided expert feedback.

An Online Machine Learning System (OMLS) includes an Online Machine Learning Engine (OMLE) for incorporating and utilizing one or more machine learning algorithms or models utilizing features to generate a result, and capable of incorporating and utilizing multiple different machine learning algorithms; wherein the OMLS is configured to perform continuous online machine learning, the continuous online machine learning comprising: continuous online machine learning from streaming data including an instance comprising a vector of inputs, the vector of inputs comprising a plurality of continuous or categorical features; and continuous online machine learning from periodically provided expert feedback.

A method for an anomaly detection is provided. The method may include acquiring a score predictor trained using normal time-series data, wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series data and the conditional score represents a gradient of data density, extracting data for a specific time and data segments corresponding to a period before the specific time from target time-series data, and predicting a conditional score for the data segments through the trained score predictor and conducting an anomaly determination for the data for the specific time using the predicted conditional score.

A method for an anomaly detection is provided. The method may include acquiring a score predictor trained using normal time-series data, wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series data and the conditional score represents a gradient of data density, extracting data for a specific time and data segments corresponding to a period before the specific time from target time-series data, and predicting a conditional score for the data segments through the trained score predictor and conducting an anomaly determination for the data for the specific time using the predicted conditional score.

Methods and systems that provide data privacy for implementing a neural network-based inference are described. A method includes injecting stochasticity into the data to produce perturbed data, wherein the injected stochasticity satisfies an -differential privacy criterion and transmitting the perturbed data to a neural network or to a partition of the neural network for inference.

Methods and systems that provide data privacy for implementing a neural network-based inference are described. A method includes injecting stochasticity into the data to produce perturbed data, wherein the injected stochasticity satisfies an -differential privacy criterion and transmitting the perturbed data to a neural network or to a partition of the neural network for inference.

An Online Machine Learning System (OMLS) including an Online Preprocessing Engine (OPrE) configured to (a) receive streaming data including an instance comprising a vector of inputs, the vector of inputs comprising a plurality of continuous or categorical features; (b) discretize features; (c) impute missing feature values; (d) normalize features; and (e) detect drift or change in features; an Online Feature Engineering Engine (OFEE) configured to produce features; and an Online Robust Feature Selection Engine (ORFSE) configured to evaluate and select features; an Online Machine Learning Engine (OMLE) configured to incorporate and utilize one or more machine learning algorithms or models utilizing features to generate a result, and capable of incorporating and utilizing multiple different machine learning algorithms or models, wherein each of the OMLE, the OPrE, the OFEE, and the ORFSE are continuously communicatively coupled to each other, and wherein the OMLS is configured to perform continuous online machine learning.

An Online Machine Learning System (OMLS) including an Online Preprocessing Engine (OPrE) configured to (a) receive streaming data including an instance comprising a vector of inputs, the vector of inputs comprising a plurality of continuous or categorical features; (b) discretize features; (c) impute missing feature values; (d) normalize features; and (e) detect drift or change in features; an Online Feature Engineering Engine (OFEE) configured to produce features; and an Online Robust Feature Selection Engine (ORFSE) configured to evaluate and select features; an Online Machine Learning Engine (OMLE) configured to incorporate and utilize one or more machine learning algorithms or models utilizing features to generate a result, and capable of incorporating and utilizing multiple different machine learning algorithms or models, wherein each of the OMLE, the OPrE, the OFEE, and the ORFSE are continuously communicatively coupled to each other, and wherein the OMLS is configured to perform continuous online machine learning.