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Analysis

1 / 6,043
1.20220180975METHODS 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.

2.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.
3.20210397895INTELLIGENT 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.

4.202011048058A SYSTEM OF TWO LEVEL MODEL FOR EARLY DETECTION OF DDOS ATTACKS ON LOT DEVICES OPTIMAL FEATURE SELECTION AND MACHINE LEARNING CLASSIFIERS
IN - 01.01.2021
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 202011048058 Applicant Ms. Vimal Gaur Inventor Ms. Vimal Gaur
This invention discloses a two level model for early detection of DDoS attack on IoT devices.For early detection of DDoS attack at level 1 various optimal features selection algorithmsnamely Chi Square, Extra Tree and ANOVA were applied and at the level 2 four classifiersnamely Random Forest, Decision Tree, KNN and XGBoost have been used. . This combinationof feature selection algorithms and classifiers boost the performance of detecting DDoS attackson IoT devices.These classifiers have been trained and tested on dataset gathered in two days (Testing set andTraining set). This leads to removal of redundant variables as key features have high featureimportance scores. It can be seen that without feature selection XGBoost has performedsignificantly better than other models by the indexes Accuracy, F1 Score, Recall, Precision.Since the decision tree and Random Forest takes shorter training time but performs worse thanXGBoost and KNN. This process is done iteratively using Chi2 as feature selection and foundXGBoost performing best by all the indexes but DT and RF still take shorter training time. Afterperforming next series of iteration with Extra Tree it has been found that XGBoost performsbetter with reduction in features from 79 to 21 (reduction in feature by 74%).XGBoost givesfairly high value of all indexes for ANOVA feature selection with only 11 features (86% featurereduction).
5.3786855AUTOMATED 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.
6.10127496System and method for estimating arrival time
US - 13.11.2018
Int.Class G06E 1/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
EOPTICAL COMPUTING DEVICES
1Devices for processing exclusively digital data
 Appl.No 15923329 Applicant BEIJING DIDI INFINITY TECHNOLOGY AND DEVELOPMENT CO., LTD. Inventor Kun Fu

Systems and methods are provided for estimating arrival time associated with a ride order. An exemplary method may comprise: inputting transportation information to a trained machine learning model. The transportation information may comprise an origin and a destination associated with the ride order, and the trained machine learning model may comprise a wide network, a deep neural network, and a recurrent neural network all coupled to a multilayer perceptron network. The method may further comprise, based on the trained machine learning model, obtaining an estimated time for arriving at the destination via a route connecting the origin and the destination.

7.20200202436Method and system using machine learning for prediction of stocks and/or other market instruments price volatility, movements and future pricing by applying random forest based techniques
US - 25.06.2020
Int.Class G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
 Appl.No 16783457 Applicant Dhruv Siddharth Krishnan Inventor Dhruv Siddharth Krishnan

A method for providing stock predictive information by a cloud-based computing system implementing a random forest algorithm via a machine learning model by receiving a set of stock data from multiple sources of stock data wherein the set of stock data at least comprises stock prices at the open and close of a market, changes in stock prices during the open and close of a market, and real-time stock data; defining a range in time contained in a window defined of an initial selected month, a day or real-time period and an end of the selected month, day and real-time period; applying the random forest model to the set of stock data by creating multiple decision trees to predict a stock price in a quantified period, amount or percentage change in a stock price; and presenting the predicted stock price in a graphic user interface to an user.

8.20190164164COLLABORATIVE PATTERN RECOGNITION SYSTEM
US - 30.05.2019
Int.Class G06Q 20/40
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
20Payment architectures, schemes or protocols
38Payment protocols; Details thereof
40Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
 Appl.No 16172751 Applicant Krishna Pasupathy Karambakkam Inventor Krishna Pasupathy Karambakkam

Apparatus and associated methods relate to a pattern recognition system configured to classify a transaction as anomalous or not anomalous as a function of a predictive analytic model configured to detect anomalies, generate a rule based on expert analysis of a limited number of data samples to classify as anomalous a transaction erroneously classified as not anomalous, augment the predictive analytic model with the generated rule, and deploy the augmented predictive analytic model to automatically identify an attack early in a live transaction stream. In some examples, the transaction may be a bank card purchase. Some transactions may be classified anomalous due to fraud, compliance violation such as money laundering, or terrorist funding. The predictive analytic model may be, for example, a decision tree followed by a regression model. Various embodiments may advantageously generate rules based on transaction criteria selected by human experts exploring and manipulating visually perceptible transaction representations.

9.20210201076Ontology matching based on weak supervision
US - 01.07.2021
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 17013688 Applicant NEC Laboratories Europe GmbH Inventor Bin Cheng

A method is for matching a set of first classes assigned to a first data set with a set of second classes assigned to a second data set. The method includes constructing, via a set of pre-processing functions, a plurality of alignment profiles such that at least one alignment profile is assigned to each of the first classes and each of the second classes. The method includes generating a comparison matrix for each group of the alignment profiles, such that each group includes at least one of the first classes and at least one of the second classes. The method includes training a first machine learning model, through supervised training, based on the generated comparison matrices and based on probabilistic labels generated by a second machine learning model.

10.WO/2023/126832DIGITAL MEDICINE COMPANION FOR CDK INHIBITOR MEDICATIONS FOR CANCER PATIENTS
WO - 06.07.2023
Int.Class G16H 10/40
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
10ICT specially adapted for the handling or processing of patient-related medical or healthcare data
40for data related to laboratory analysis, e.g. patient specimen analysis
 Appl.No PCT/IB2022/062810 Applicant PFIZER INC. Inventor CAI, Xuemei
The present disclosure relates to a digital medicine companion for patients undergoing oncology treatments. A patient may enter symptoms on a prescription software. A wearable device may passively collect other healthcare data such as biological data and/or physical activity data. The patient may therefore be monitored using the prescription software and/or wearables. Furthermore, the prescription software may be integrated with biofluid testing systems. For example, an at-home biofluid monitoring kit and/or a laboratory system may communicate with the prescription software and/or its backend server. The healthcare data collected through the monitoring and the biofluid testing may be fed into a machine learning model, which may output whether the patient is likely to develop side effects such as cytopenia. One or more alert notifications, e.g., to a clinician dashboard and/or to the prescription software, may be triggered when the machine learning model determines a higher likelihood of such side effects.
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