Machine learning

SYSTEMS AND METHODS FOR COMPLETION OPTIMIZATION FOR WATERFLOOD ASSETS

Absstract of: AU2022381046A1

Implementations described and claimed herein provide systems and methods for a framework to achieve completion optimization for waterflood field reservoirs. The proposed methodology leverages adequate data collection, preprocessing, subject matter expert knowledge-based feature engineering for geological, reservoir and completion inputs, and state-of-the-art machine-learning technologies, to indicate important production drivers, provide sensitivity analysis to quantify the impacts of the completion features, and ultimately achieve completion optimization. In this analytical framework, model-less feature ranking based on mutual information concept and model-dependent sensitivity analyses, in which a variety of machine-learning models are trained and validated, provides comprehensive multi-variant analyses that empower subject-matter experts to make a smarter decision in a timely manner.

Deep Learning Models For Region Of Interest Determination

Absstract of: US2024170149A1

A method of determining a region of interest in an image of tissue of an individual by an apparatus including processing circuitry may include executing, by the processing circuitry, instructions that cause the apparatus to partition an image of tissue of an individual into a set of areas, identify a tissue type of each area of the image, and apply a classifier to the image to determine a region of interest, the classifier being configured to determine regions of interest based on the tissue types of the set of areas of the image.

System, Method, and Computer Program Product for Interpreting Black Box Models by Perturbing Transaction Parameters

Absstract of: US2024169352A1

A computer-implemented method includes: receiving an inquiry request message identifying a first payment transaction having a plurality of transaction parameters and a risk score, where the risk score is generated by a machine-learning model based on the plurality of transaction parameters; for each transaction parameter of the plurality of transaction parameters, perturbing a value of the transaction parameter and re-analyzing the first payment transaction with the machine-learning model to generate a perturbed risk score based on the perturbed transaction parameter; determining at least one impact parameter from the plurality of transaction parameters by comparing the perturbed risk scores generated for each of the plurality of transaction parameters; and generating an inquiry response message based on the at least one impact parameter.

TRANSFER LEARNING METHOD FOR A MACHINE LEARNING SYSTEM

Absstract of: CN117751372A

There is provided a transfer learning method for a system comprising a plurality of existing agents and a new agent, the plurality of existing agents each comprising a trained machine learning model for modeling a respective existing machine learning scene. The system includes a database comprising: available models for modeling a scene, including trained models; existing scene metadata indicating an existing scene; and transfer learning data indicative of a portion of the trained model. The method includes receiving new scene metadata indicating a new scene to be modeled by a new agent and receiving new scene training data for training a model of the new scene. The method also includes querying the database to: select an available model to model the new scene based on the received data; and selecting at least some of the transfer learning data to train the selected model based on the received data. The method includes training the selected model using the new scene training data and the selected transfer learning data.

MACHINE LEARNING TRAINING DURATION CONTROL

Absstract of: WO2024102233A1

Some embodiments select a machine learning model training duration based at least in part on a fractal dimension calculated for a training data dataset. Model training durations are based on one or more characteristics of the data, such as a fractal dimension, a data distribution, or a spike count. Default long training durations are sometimes replaced by shorter durations without any loss of model accuracy. For instance, the time-to-detect for a model-based intrusion detection system is shortened by days in some circumstances. Model training is performed per a profile which specifies particular resources or particular entities, or both. Realistic test data is generated on demand. Test data generation allows the trained model to be exercised for demonstrations, or for scheduled confirmations of effective monitoring by a model-based security tool, without thereby altering the model's training.

SYSTEM AND METHOD OF PREDICTION OF PRESENCE/ABSENCE FOR THE THREE GENERA OF LARVAE (CULEX, AEDES, ANOPHELES) IN BREEDING SITES

Absstract of: WO2024100428A1

System and method for determining the presence/absence of larvae for the three genera (Culex, Aedes, Anopheles) per breeding site, using a cloud server for processing the collected inspection and geospatial data and applying Machine Learning (RF/ XGBoost) algorithms.

SOFTWARE ASSESSMENT TOOL FOR MIGRATING COMPUTING APPLICATIONS USING MACHINE LEARNING

Absstract of: WO2024102695A1

A computing system includes a processor; and a memory having stored thereon instructions that, when executed by the one or more processors, cause the system to: receive content migration project parameters, resource migration project parameters and one or more services parameters of a user; scan a tenant computing environment; process the parameters by applying a multiplier display the costs, profits and pricing information. A method includes receiving content migration project parameters, resource migration projecting parameters and one or more services parameters of a user; scanning a tenant computing environment; processing the parameters by applying a multiplier displaying the costs, profits and pricing information. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to: receive content migration project parameters, resource migration project parameters and one or more services parameters of a user; scan a tenant computing environment; process the parameters by applying a multiplier display the costs, profits and pricing information.

PROCESS MAPPING AND MONITORING USING ARTIFICIAL INTELLIGENCE

Absstract of: US2024160550A1

The disclosure describes a system for the advanced delivery of information. In some embodiments, the system creates a display in response to an alarm. In some embodiments, the information on the display is a function of attribute mapping and/or analysis performed by the system. The system uses one or more of manual links, statistical analysis, correlations, maintence data, and/or historical data as tools during determination of what to display according to some embodiments. In some embodiments, the system uses one or more of these tools in conjunction with one or more of process simulators, artificial intelligence, machine learning, and/or real process feedback in the analysis to determine what to display to a user during an emergency and/or an anomalous event.

SYSTEM CONFIGURED TO DETECT AND BLOCK THE DISTRIBUTION OF MALICIOUS CONTENT THAT IS ATTRIBUTABLE TO AN ENTITY

Absstract of: WO2024102310A1

An online portal receives digital content from a user device. The online portal is communicably coupled to a computer server hosting an online media service in the public or non-public domain. The user device is associated with an online account on the online media service. Based on the digital content, at least one requirement associated with the online account is identified. One or more respondent services are determined satisfy the requirement. By each respondent service, the digital content is processed using a respective machine learning model trained, based on user-attributable content, to generate a respondent evaluation. A quorum of respondent evaluations is generated. The quorum of respondent evaluations is determined to achieve a respondent consensus. Responsive to determining that the respondent consensus satisfies an approval condition, the digital content is sent from the online portal to the computer server for posting the digital content on the online media service.

SYSTEMS AND METHODS FOR ENHANCED MACHINE LEARNING TECHNIQUES FOR KNOWLEDGE MAP GENERATION AND USER INTERFACE PRESENTATION

Absstract of: WO2024102449A1

Systems and methods for extracting information from documents and constructing corresponding knowledge maps with respect to defined knowledge models. Deep-learningbased models for Natural Language Processing (NLP) are applied to tokenize words, tag, parse, and lemmatize sentences of input documents. Then an information extractor traverses the dependency tree of NLP object to recursively extract the entities of interest to the knowledge models. Finally, a knowledge map constructor traverses the dependency tree of NLP object to determine the relationships among the extracted entities and construct knowledge maps recursively following the defined knowledge models.

SYSTEM AND METHOD FOR BUILDING MACHINE LEARNING OR DEEP LEARNING DATA SETS FOR RECOGNIZING LABELS ON ITEMS

Absstract of: US2024158123A1

This application relates to a method and a system for building machine learning or deep learning data sets for automatically recognizing labels on items. The system may include an optical scanner configured to capture an item including one or more labels provided thereon, the item captured a plurality of times at different positions with respect to the optical scanner. The system may further include a robotic arm on which the item is disposed, the robotic arm configured to rotate the item horizontally and/or vertically such that the one or more labels of the item are captured by the optical scanner at different positions with respect to the optical scanner. The system may include a database configured to store the captured images.

MACHINE LEARNING MODEL REGISTRY

Absstract of: US2024161018A1

Systems and methods to utilize a machine learning model registry are described. The system deploys a first version of a machine learning model and a first version of an access module to server machines. Each of the server machines utilizes the model and the access module to provide a prediction service. The system retrains the machine learning model to generate a second version. The system performs an acceptance test of the second version of the machine learning model to identify it as deployable. The system promotes the second version of the machine learning model by identifying the first version of the access module as being interoperable with the second version of the machine learning model and by automatically deploying the first version of the access module and the second version of the machine learning model to the plurality of server machines to provide the prediction service.

USING MACHINE LEARNING FOR ICONOGRAPHY RECOMMENDATIONS

Absstract of: US2024161367A1

In some implementations, a recommendation system may input text into a machine learning model that was trained using input specific to an organization associated with the text and was refined using input specific to a portion of the organization. The recommendation system may receive, from the machine learning model, a recommendation indicating one or more visual components, stored in a database associated with the organization, to use with the text. The machine learning model may use natural language processing and sentiment detection to parse the text. Accordingly, the recommendation system may receive the one or more visual components from the database and generate an initial draft including the text and the one or more visual components.

ENTERPRISE DOCUMENT CLASSIFICATION

Absstract of: US2024160768A1

A collection of documents or other files and the like within an enterprise network are labelled according to an enterprise document classification scheme, and then a recognition model such as a neural network or other machine learning model can be used to automatically label other files throughout the enterprise network. In this manner, documents and the like throughout an enterprise can be automatically identified and managed according to features such as confidentiality, sensitivity, security risk, business value, and so forth.

ADD-ON TO A MACHINE LEARNING MODEL FOR INTERPRETATION THEREOF

Absstract of: US2024161005A1

There is provided an add-on component configured for: receiving features and an outcome of an ML model, wherein at least two of the features are correlated by a covariance value above a threshold, computing, for each of the features, a respective contribution coefficient denoting an initial value, identifying a certain feature with highest contribution coefficient indicative of a relative contribution to the outcome, computing, for a subset of features that are non-independent with respect to the certain feature, a respective subsequent value for the contribution coefficient by adjusting the respective initial value according to a covariance with the contribution coefficient of the certain feature, iterating the identifying and the computing to compute a subsequent certain feature with highest contribution coefficient for the remaining features, and re-adjusting the respective contributing coefficient according to a covariance with the contribution coefficient of the subsequent certain feature, and providing the respective contribution coefficient(s).

NEXT-GENERATION MOLECULAR PROFILING

Absstract of: EP4369356A2

Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. This approach has been applied to identify biomarker signatures that strongly correlate with response of colorectal cancer patients to FOLFOX. Described herein are data structures, data processing, and machine learning models to predict effectiveness of a treatment for a disease or disorder of a subject having a particular set of biomarkers, as well as an exemplary application of such a model to precision medicine, e.g., to methods for selecting a treatment based on a molecular profile, e.g., a treatment comprising administration of 5-fluorouracil/leucovorin combined with oxaliplatin (FOLFOX) or with irinotecan (FOLFIRI).

MACHINE LEARNING-BASED RISK STRATIFICATION AND MANAGEMENT OF NON-ALCOHOLIC FATTY LIVER DISEASE

Absstract of: WO2024097993A1

Screening and risk-stratification of patients at risk for developing liver disease, such as non-alcoholic fatty liver disease ("NAFLD"), among others, is achieved by applying an optimized set of patient health data features to a suitably trained machine learning algorithm or model. The machine learning model outputs NAFLD risk score data that quantify or otherwise indicate a risk of the patient developing NAFLD based on features present in their patient health data. The NAFLD risk score data can be further analyzed to risk stratify the patient and assist with determining next steps in the healthcare workflow for the patient.

METHOD AND ELECTRONIC DEVICE FOR SECURE ON-DEVICE STORAGE FOR MACHINE LEARNING MODELS

Absstract of: US2024152283A1

A method for performing an inference includes: detecting a context among at least one context associated with at least one application; triggering a model execution command to a smart agent of an electronic device, based on the detected context; loading a machine learning (ML) model into a secure storage of the electronic device, based on the detected context and the triggered model execution command; generating, using the loaded ML model, an inference, based on data associated with the detected context; and sharing the generated inference with each application of the at least one application that is registered for the detected context.

SYSTEM, APPARATUS, AND METHOD FOR STRUCTURING DOCUMENTARY DATA FOR IMPROVED TOPIC EXTRACTION AND MODELING

Absstract of: US2024152538A1

A method of structuring entity data using a machine learning document profiling model for improved information extraction comprises: training a learning document profiling model by applying document data and identification information on different document types to the learning document profiling model; profiling entity document data using the trained learning document profiling model; generating an entity profile using the trained learning document profiling model based on the profiled document data; selecting a subset of documents comprised in the profiled document data based on the entity profile; and tagging the selected subset of documents for further processing comprising one or more of a topic extraction process, a document processing algorithm selection process, a topic importance rating process, a knowledge graph mapping process, a document signature generation process, a document querying process, and a language derivation process.

GENERATING ENCODED TEXT BASED ON SPOKEN UTTERANCES USING MACHINE LEARNING SYSTEMS AND METHODS

Absstract of: US2024152684A1

Systems and methods for generating encoded text representations of spoken utterances are disclosed. Audio data is received for a spoken utterance and analyzed to identify a nonverbal characteristic, such as a sentiment, a speaking rate, or a volume. An encoded text representation of the spoken utterance is generated, comprising a text transcription and a visual representation of the nonverbal characteristic. The visual representation comprises a geometric element, such as a graph or shape, or a variation in a text attribute, such as font, font size, or color. Analysis of the audio data and/or generation of the encoded text representation can be performed using machine learning.

MACHINE LEARNING TRAINING DURATION CONTROL

Absstract of: US2024152798A1

Some embodiments select a machine learning model training duration based at least in part on a fractal dimension calculated for a training data dataset. Model training durations are based on one or more characteristics of the data, such as a fractal dimension, a data distribution, or a spike count. Default long training durations are sometimes replaced by shorter durations without any loss of model accuracy. For instance, the time-to-detect for a model-based intrusion detection system is shortened by days in some circumstances. Model training is performed per a profile which specifies particular resources or particular entities, or both. Realistic test data is generated on demand. Test data generation allows the trained model to be exercised for demonstrations, or for scheduled confirmations of effective monitoring by a model-based security tool, without thereby altering the model's training.

PROACTIVELY DETECTING AND PREDICTING POTENTIAL BREAKAGE OR SUPPORT ISSUES FOR IMPENDING CODE CHANGES

Absstract of: US2024152784A1

In some implementations, a regression prediction platform may obtain one or more feature sets related to an impending code change, wherein the one or more feature sets may include one or more features related to historical code quality for a developer associated with the impending code change or a quality of a development session associated with the impending code change. The regression prediction platform may provide the one or more feature sets to a machine learning model trained to predict a risk associated with deploying the impending code change based on a probability that deploying the impending code change will cause breakage after deployment and/or a probability that the impending code change will cause support issues after deployment. The regression prediction platform may generate one or more recommended actions related to the impending code change based on the risk associated with deploying the impending code change.

INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING METHOD, AND PROGRAM

Absstract of: US2024152788A1

The present technology relates to an information processing system, an information processing method, and a program that are capable of facilitating the understanding of an explanation regarding AI processing. The information processing system includes an evaluation portion that evaluates multiple choices according to two or more evaluation criteria based on parameters obtained during a process or a result of machine learning, and an explanation portion that generates explanatory text regarding each of the choices by using a phrase associated with a corresponding one of the evaluation criteria. For example, the present technology is applicable to hardware and software performing various processes with the use of AI.

ARTIFICIAL-INTELLIGENCE-ASSISTED CONSTRUCTION OF INTEGRATION PROCESSES

Absstract of: US2024152811A1

A substantial learning curve is required to construct integration processes in an integration platform. This can make it difficult for novice users to construct effective integration processes, and for expert users to construct integration processes quickly and efficiently. Accordingly, embodiments for building and operating a model to predict next steps, during construction of an integration process via a graphical user interface, are disclosed. The model may comprise a Markov chain, prediction tree, or an artificial neural network (e.g., graph neural network, recurrent neural network, etc.) or other machine-learning model that predicts a next step based on a current sequence of steps. In addition, the graphical user interface may display the suggested next steps according to a priority (e.g., defined by confidence values associated with each step).

MACHINE LEARNING MONITORING SYSTEMS AND METHODS

Nº publicación: US2024152810A1 09/05/2024

Applicant:

ARTHUR AI INC [US]
Arthur AI, Inc

Absstract of: US2024152810A1

A method for monitoring performance of a ML system includes receiving a data stream via a processor and generating a first plurality of metrics based on the data stream. The processor also generates input data based on the data stream, and sends the input data to a machine learning (ML) model for generation of intermediate output and model output based on the input data. The processor also generates a second plurality of metrics based on the intermediate output, and a third plurality of metrics based on the model output. An alert is generated based on at least one of the first plurality of metrics, the second plurality of metrics, or the third plurality of metrics, and a signal representing the alert is sent for display to a user via an interface.