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Analysis

1 / 1,382
1.20210397895INTELLIGENT LEARNING SYSTEM WITH NOISY LABEL DATA
US - 23.12.2021
Int.Class G06K 9/62
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KGRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for recognising patterns
62Methods or arrangements for pattern recognition using electronic means
 Appl.No 16946465 Applicant INTERNATIONAL BUSINESS MACHINES CORPORATION Inventor Yang SUN

Various embodiments are provided for providing machine learning with noisy label data in a computing environment using one or more processors in a computing system. A label corruption probability of noisy labels may be estimated for selected data from a dataset using temporal inconsistency in a machine model prediction during a training operation in a neural network.

2.20220180975METHODS AND SYSTEMS FOR DETERMINING GENE EXPRESSION PROFILES AND CELL IDENTITIES FROM MULTI-OMIC IMAGING DATA
US - 09.06.2022
Int.Class G16B 40/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
30Unsupervised data analysis
 Appl.No 17553691 Applicant The Broad Institute, Inc. Inventor Aviv Regev

The present disclosure relates to systems and method of determining transcriptomic profile from omics imaging data. The systems and methods train machine learning methods with intrinsic and extrinsic features of a cell and/or tissue to define transcriptomic profiles of the cell and/or tissue. Applicants utilize a convolutional autoencoder to define cell subtypes from images of the cells.

3.WO/2023/059663SYSTEMS AND METHODS FOR ASSESSMENT OF BODY FAT COMPOSITION AND TYPE VIA IMAGE PROCESSING
WO - 13.04.2023
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
 Appl.No PCT/US2022/045706 Applicant THE BROAD INSTITUTE, INC. Inventor KHERA, Amit
The subject matter disclosed herein relates to utilizing the silhouette of an individual to measure body fat volume and distribution. Particular examples relates to providing a system, a computer-implemented method, and a computer program product to utilize a binary outline, or silhouette, to predict the individual's fat depot volumes with machine learning models.
4.20200272947Orchestrator for machine learning pipeline
US - 27.08.2020
Int.Class G06F 15/173
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
15Digital computers in general; Data processing equipment in general
16Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
163Interprocessor communication
173using an interconnection network, e.g. matrix, shuffle, pyramid, star or snowflake
 Appl.No 16284291 Applicant SAP SE Inventor Lukas Carullo

Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

5.12274503Myopia ocular predictive technology and integrated characterization system
US - 15.04.2025
Int.Class A61B 3/14
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
3Apparatus for testing the eyes; Instruments for examining the eyes
10Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
14Arrangements specially adapted for eye photography
 Appl.No 18778027 Applicant COGNITIVECARE INC. Inventor Venkata Narasimham Peri

According to an embodiment, disclosed is a system comprising a processor wherein the processor is configured to receive an input data comprising an image of an ocular region of a user, clinical data of the user, and external factors; extract, using an image processing module comprising adaptive filtering techniques, ocular characteristics, combine, using a multimodal fusion module, the input data to determine a holistic health embedding; detect, based on a machine learning model and the holistic health embedding, a first output comprising likelihood of myopia, and severity of myopia; predict, based on the machine learning model and the holistic health embedding, a second output comprising an onset of myopia and a progression of myopia in the user; and wherein the machine learning model is a pre-trained model; and wherein the system is configured for myopia prognosis powered by multimodal data.

6.3786855AUTOMATED DATA PROCESSING AND MACHINE LEARNING MODEL GENERATION
EP - 03.03.2021
Int.Class G06N 5/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computing arrangements using knowledge-based models
 Appl.No 19290076 Applicant ACCENTURE GLOBAL SOLUTIONS LTD Inventor HIGGINS LUKE
A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.
7.20140180975INSTANCE WEIGHTED LEARNING MACHINE LEARNING MODEL
US - 26.06.2014
Int.Class G06N 99/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
99Subject matter not provided for in other groups of this subclass
 Appl.No 13725653 Applicant INSIDESALES.COM, INC. Inventor Martinez Tony Ramon

An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.

8.2013364041Instance weighted learning machine learning model
AU - 09.07.2015
Int.Class G06F 15/18
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
15Digital computers in general; Data processing equipment in general
18in which a program is changed according to experience gained by the computer itself during a complete run; Learning machines
 Appl.No 2013364041 Applicant InsideSales.com, Inc. Inventor Martinez, Tony Ramon
An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.
9.WO/2014/100738INSTANCE WEIGHTED LEARNING MACHINE LEARNING MODEL
WO - 26.06.2014
Int.Class G06F 15/18
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
15Digital computers in general; Data processing equipment in general
18in which a program is changed according to experience gained by the computer itself during a complete run; Learning machines
 Appl.No PCT/US2013/077260 Applicant INSIDESALES.COM, INC. Inventor MARTINEZ, Tony, Ramon
An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.
10.20230206137Orchestrator for machine learning pipeline
US - 29.06.2023
Int.Class G06F 15/173
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
15Digital computers in general; Data processing equipment in general
16Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
163Interprocessor communication
173using an interconnection network, e.g. matrix, shuffle, pyramid, star or snowflake
 Appl.No 18111839 Applicant SAP SE Inventor Lukas Carullo

Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

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