Machine learning

MACHINE LEARNING MODEL TRAINED BASED ON MUSIC AND DECISIONS GENERATED BY EXPERT SYSTEM

Absstract of: WO2024178038A1

Techniques are disclosed that pertain to training a machine learning model to generate audio data similar to a music generator program. A computer system, executing a rules-based music generator program, selects and combines multiple musical expressions to generate audio data. The computer system trains a machine learning model to select and combine musical expressions to generate music compositions. The machine learning model receives generator information by the generator program that indicates expression selection decisions to generate the audio data, mixing decisions to generate the audio data, and first audio information output based on the generator program's expression selection decisions and the mixing decisions. The computer system compares the generator information to expression selection decisions, mixing decisions, and second audio information generated by the machine learning model based on the machine learning model's expression selection decisions and mixing decisions. The computer system updates the machine learning model based on the comparing.

TARGETED ADVERTISEMENT RANKING USING MACHINE LEARNING

Absstract of: WO2024177749A1

The present disclosure relates to ranking electronic advertisements using one or more machine learning algorithms for a targeted audience associated with an aircraft flight.

BATTERY CHARGE AND DISCHARGE PROFILE ANALYSIS METHOD, AND BATTERY CHARGE AND DISCHARGE PROFILE ANALYSIS APPARATUS

Absstract of: US2024288500A1

A method and apparatus for battery charge/discharge profile analysis is provided. The method includes training a machine learning model using a plurality of training charge/discharge profiles as a training dataset, where each training charge/discharge profile includes training section classification information, and the training section classification information is a dataset in which an identification number of any one of a plurality of charge/discharge control sections performed in a sequential order in an activation process is allocated to each time index; inputting a target charge/discharge profile acquired through the activation process of a battery cell to the machine learning model; and acquiring target section classification information for the input target charge/discharge profile from the machine learning model. The target section classification information is a dataset in which the identification number of any one of the plurality of charge/discharge control sections is allocated to each time index of the target charge/discharge profile.

FORMULATION GRAPH FOR MACHINE LEARNING OF CHEMICAL PRODUCTS

Absstract of: US2024290440A1

Chemical formulations for chemical products can be represented by digital formulation graphs for use in machine learning models. The digital formulation graphs can be input to graph-based algorithms such as graph neural networks to produce a feature vector, which is a denser description of the chemical product than the digital formulation graph. The feature vector can be input to a supervised machine learning model to predict one or more attribute values of the chemical product that would be produced by the formulation without actually having to go through the production process. The feature vector can be input to an unsupervised machine learning model trained to compare chemical products based on feature vectors of the chemical products. The unsupervised machine learning model can recommend a substitute chemical product based on the comparison.

METHODS FOR IDENTIFYING MUTATIONS USING MACHINE LEARNING

Absstract of: US2024290422A1

Disclosed herein are methods for identifying mutations from a patient sample, by evaluating, using a computer having a machine learning classifier, a candidate variant against a plurality of decision trees trained to detect mutations in the candidate variant with a gradient boosting algorithm.

SYSTEM AND METHOD FOR IMPROVING HUMAN-CENTRIC PROCESSES

Absstract of: US2024289724A1

A system and method of generating a plurality of actionable insights is disclosed herein. A computing system retrieves data corresponding to a work procedure. Each work procedure includes a plurality of steps. The computing system generates a predictive model for each actionable insight using a plurality of machine learning models by generating an input training based on the retrieved work procedure data and learning, by the plurality machine learning models, a metric corresponding to each actionable insight based on each respective input training set. The input data set for each actionable insight includes actionable insight specific information. The computing system receives a request to generate a plurality of actionable insights for a current work procedure. The computing system generates, via the predictive models, a plurality of metrics for a plurality of actionable insights based on data corresponding to the current work procedure.

SYSTEM AND METHOD FOR PREDICTING IDEAL TIMES TO CALL

Absstract of: US2024289681A1

A device comprises a processor. The processor is configured to: generate training vectors based on data related to communication with users; convert the training vectors into optimized vectors to be input into a machine learning unit; apply the machine learning unit to the optimized vectors to construct decision trees for determining probabilities of making a successful call during different time windows; generate a list pf calls and calling times based on the determined probabilities; and forward the list to an automatic dialer.

TARGETED ADVERTISEMENT RANKING USING MACHINE LEARNING

Absstract of: US2024289839A1

The present disclosure relates to ranking electronic advertisements using one or more machine learning algorithms for a targeted audience associated with an aircraft flight. For example, one or more embodiments described herein include a computer-implemented method comprising executing a learn-to-rank algorithm to train a machine learning model on a training dataset that includes electronic advertisements with associated scores characterizing a relevancy between the electronic advertisements and a defined query. The computer-implemented method can also comprise applying the trained machine learning model to rank a set of electronic advertisements based on a feature vector characterizing input data that includes flight details of an aircraft.

EFFICIENT HIDDEN MARKOV MODEL ARCHITECTURE AND INFERENCE RESPONSE

Absstract of: US2024289594A1

Certain aspects of the present disclosure provide techniques and apparatus for improved hidden Markov model (HMM)-based machine learning. A sequence of observations is accessed. A hidden Markov model (HMM) comprising a set of transition probabilities, a set of emission probabilities, a transition coefficient hyperparameter, and an emission coefficient hyperparameter is also accessed, and a first output inference from the HMM is generated based on the sequence of observations.

IDENTIFYING RECURRING EVENTS USING AUTOMATED SEMI-SUPERVISED CLASSIFIERS

Absstract of: US2024289688A1

Systems and methods for training machine learning models are disclosed. An example method includes receiving historical event timing data including event data for a first portion including events from a first time period, and a second portion comprising events from a second time period not including the first time period, predicting, based on the first portion of the historical event timing data, a first plurality of predicted events, the first plurality of predicted events corresponding to the second time period, determining a first subset of predicted events to be accurate predictions based at least in part on comparing the first plurality of predicted events to the historical events occurring within the second time period, generating training data based at least in part on the first subset of the first plurality of predicted events, and training the machine learning model based at least in part on the training data.

AI-DRIVEN TRANSACTION MANAGEMENT SYSTEM

Absstract of: US2024289325A1

A largely automated method of categorizing spend data is provided that does not require a prior in-depth knowledge of an organization's transactional data. Natural language processing is applied to text data from transactional data to generate a consolidated cleaned data set (CDS) containing information for categorization. Logs for transactions are clustered based on similarity, forming the minimal data set (MDS). An automated algorithm selects a subset of high-value clusters that are categorized by requesting users to manually categorize one or more representative logs from each cluster of the subset. A model is then trained using the subset of manually categorized clusters and used to predict spend categories for the remaining logs with high accuracy. The AI engine automatically analyzes the predictions based on client context and either auto-tunes the machine learning model or identifies a new subset of clusters to be manually categorized. This loop may continue until 95%-100% of the spend is categorized.

ARTIFICIAL INTELLIGENCE BASED RULE GENERATION FOR DATABASE CHANGE DEPLOYMENT

Absstract of: US2024289307A1

Embodiments provide systems, methods, and computer program products that utilize artificial intelligence/machine learning to process database change data and correlated performance data to predict the impact of database changes and generate rules with respect to database changes to prevent undesired behavior or promote increased performance.

SYSTEMS AND METHODS FOR AUTOMATED DATA SET MATCHING SERVICES

Absstract of: US2024289364A1

The present solution provides systems and methods to receive a data set comprising a representation of one or more questions from a survey and provide the data set as input to each of a plurality of machine learning models trained to predict a domain associated with the one or more questions. The systems and methods can receiving as output a first domain prediction for the domain from each of the plurality of machine learning models and determine a second domain prediction for the domain for each question of the one or more questions based on applying a function to each of the first domain predictions. The systems and methods can select, based on the data set and the second domain prediction, an enumerated list of one or more answers from an answer set and cause a display of the enumerated list via a user interface for a selection.

MULTISTAGE ANALYSIS OF EMAILS TO IDENTIFY SECURITY THREATS

Absstract of: US2024291834A1

Access to emails delivered to an employee of an enterprise is received. An incoming email addressed to the employee is acquired. A primary attribute is extracted from the incoming email by parsing at least one of: (1) content of the incoming email or (2) metadata associated with the incoming email. It is determined whether the incoming email deviates from past email activity, at least in part by determining, as a secondary attribute, a mismatch between a previous value for the primary attribute and a current value for the primary attribute, using a communication profile associated with the employee, and providing a measured deviation to at least one machine learning model.

METHOD AND SYSTEM FOR AUTOMATICALLY GENERATING AN INDIVIDUALISED SPORTS TRAINING SCHEDULE

Absstract of: WO2024175595A1

The invention relates to a method (500) for automatically generating an individualised electronic sports training schedule (100-1, 100-2) comprising a plurality of sessions for improving the physical performance of a user, the method comprising: a step (510) of collecting individual data of the user via a personal electronic device (200); a step (520) of analysing the collected data by means of machine learning algorithms on a digital analysis platform (300); and a step (540) of generating a first individualised schedule (100-1) on the basis of the collected and analysed data; a step (550) of measuring in real-time, at least during a session, physiological or performance parameters of the user, and/or of modifying the individual data; a step (560) of dynamically adapting the schedule on the basis of the parameters and/or the data of the preceding step by executing machine learning algorithms; and a step (570) of generating a second individualised schedule (100-2) that is better suited to the user.

DISEASE PREDICTION DEVICE, LEARNING MODEL GENERATION DEVICE, DISEASE PREDICTION METHOD, LEARNING MODEL GENERATION METHOD, AND COMPUTER-READABLE RECORDING MEDIUM

Absstract of: WO2024177033A1

A learning model generation device 10 comprises: a graph generation unit 11 which generates, from a data group including biometric information of persons and information indicating the presence or absence of occurrence of diseases in the persons, a graph composed of nodes representing data points and edges representing relationships between the nodes; a graph supplementation unit 12 which supplements the generated graph for a deficiency therein; and a model generation unit 13 which generates, from the supplemented graph, a data group in which the deficiency is supplemented, performs machine learning using the generated data group as training data, and generates a prediction model for predicting the occurrence of diseases in a person.

SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED DATA MATCHING AND RECONCILIATION OF INFORMATION

Absstract of: US2023120826A1

A method for machine learning-based data matching and reconciliation may include: ingesting a plurality of records from a plurality of data sources; identifying company associated with each of the plurality of records; assigning a unique identifier to each uniquely identified company; matching each of the records to one of the uniquely identified companies using a trained company matching machine learning engine; identifying a primary company record in the matching records and associating other matching records with the primary company record; matching each of the records to a contact using a trained contact matching machine learning engine; identifying a primary contact record in the matching records and associating other matching records with the primary contact record; synchronizing the plurality of records in a graph database using the unique identifier; receiving feedback on the matching companies and/or matching contacts; and updating the trained company matching machine learning engine.

TRAINING A COMPUTING MODULE IMPLEMENTING A MACHINE LEARNING MODEL

Absstract of: EP4421693A1

Methods are provided of training a computing module implementing a Machine Learning (ML) model, said methods comprising designating a main computing-node, and performing an iteration loop until an ending condition is satisfied, each of the iterations including: training the computing-module with ML model update using coordinates of a first selection of data-elements in the main computing-node; designating as candidates those computing-nodes parent-child related with the main computing-node based on a candidate-designation policy; calculating, for each of the candidate computing-nodes, a gradient denoting a measure of loss of the ML model using coordinates of a second selection of data-elements in the candidate; and designating as main computing-node one of the candidates depending on the gradients previously calculated, so as to initiate next iteration with said new main computing-node. Training systems and computer programs suitable for performing such training methods are also provided.

INTELLIGENT DETECTION OF FIBER COMPOSITION OF TEXTILES THROUGH AUTOMATED ANALYSIS BY MACHINE LEARNING MODELS

Absstract of: US2024281713A1

Introduced here is a process for training a machine learning model to predict a characteristic of a textile through analysis of spectral information. The spectral information may include spectra generated by a spectroscopy instrument, for example. The machine learning model can be used for a variety of applications, including automated sorting of textiles for recycling and automated analyzing of textiles for quality control.

SYSTEMS AND METHODS FOR LOCATION THREAT MONITORING

Absstract of: US2024281658A1

Disclosed is a new location threat monitoring solution that leverages deep learning (DL) to process data from data sources on the Internet, including social media and the dark web. Data containing textual information relating to a brand is fed to a DL model having a DL neural network trained to recognize or infer whether a piece of natural language input data from a data source references an address or location of interest to the brand, regardless of whether the piece of natural language input data actually contains the address or location. A DL module can determine, based on an outcome from the neural network, whether the data is to be classified for potential location threats. If so, the data is provided to location threat classifiers for identifying a location threat with respect to the address or location referenced in the data from the data source.

UNCERTAINTY ESTIMATION USING UNINFORMATIVE FEATURES

Absstract of: US2024281701A1

In some aspects, a computing system may generate uninformative features that may be added to a dataset of real features to use as a baseline for determining the quality of an explanation of model output. The uninformative features may be features that do not correlate with what a model is tasked with predicting (e.g., the uninformative features may be random values), and the real features may be informative and correlate with what the model is tasked with predicting (e.g., variables of a dataset sample). A machine learning model may be trained on a dataset that includes both the real features and the uninformative features. The computing system may generate feature attributions for model output, which may include feature attributions for the uninformative features and the real features in the dataset.

INTELLIGENT DETECTION OF FIBER COMPOSITION OF TEXTILES THROUGH AUTOMATED ANALYSIS BY MACHINE LEARNING MODELS

Absstract of: US2024281713A1

Introduced here is a process for training a machine learning model to predict a characteristic of a textile through analysis of spectral information. The spectral information may include spectra generated by a spectroscopy instrument, for example. The machine learning model can be used for a variety of applications, including automated sorting of textiles for recycling and automated analyzing of textiles for quality control.

MACHINE LEARNING SYSTEMS AND METHODS FOR ASSESSMENT, HEALING PREDICTION, AND TREATMENT OF WOUNDS

Absstract of: US2024281966A1

Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

ASSESSMENT OF TRUSTWORTHINESS OF A TRAINED MACHINE LEARNING ENTITY

Absstract of: WO2024170093A1

There are provided measures for assessment of trustworthiness of a trained machine learning entity. Such measures exemplarily comprise generating at least one artificial intelligence or machine learning trustworthiness sub-factor related metric based on an artificial intelligence or machine learning trustworthiness related behavior of an artificial intelligence or machine learning model, and transmitting an artificial intelligence or machine learning trustworthiness assessment information message comprising said at least one artificial intelligence or machine learning trustworthiness sub-factor related metric.

GENERATION OF DIGITAL STANDARDS USING MACHINE-LEARNING MODEL

Nº publicación: US2024281675A1 22/08/2024

Applicant:

SAE INT [US]
SAE International

Absstract of: US2024281675A1

One embodiment provides a method for generating a digital standard utilizing a trained machine-learning model, the method including: training at least one machine-learning model to generate digital standards from underlying standards utilizing a schema, wherein the training includes: receiving, for unstructured information within the underlying standards, a plurality of annotated underlying standards including a set of underlying standards having annotations identifying a classification of conceptual units within the set of underlying standards and corresponding to the schema; and teaching, for structured information within the underlying standards, the at least one machine-learning model patterns delineating information as belonging to conceptual units within the schema; and deploying the at least one trained machine-learning model to convert a second set of underlying standards to the digital standards, wherein the second set of underlying standards is different than the set of underlying standards. Other aspects are described and claimed.