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Analysis

1 / 1,587
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.

2.12274503Myopia 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.

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.
4.20220171996Shuffling-type gradient method for training machine learning models with big data
US - 02.06.2022
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 17109112 Applicant INTERNATIONAL BUSINESS MACHINES CORPORATION Inventor Lam Minh Nguyen

A computer-implemented method for a shuffling-type gradient for training a machine learning model using a stochastic gradient descent (SGD) includes the operations of uniformly randomly distributing data samples or coordinate updates of a training data, and calculating the learning rates for a no-shuffling scheme and a shuffling scheme. A combined operation of the no-shuffling scheme and the shuffling scheme of the training data is performed using a stochastic gradient descent (SGD) algorithm. The combined operation is switched to performing only the shuffling scheme from the no-shuffling scheme based on one or more predetermined criterion; and training the machine learning models with the training data based on the combined no-shuffling scheme and shuffling scheme.

5.20200202436Method 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.

6.WO/2012/011940GENERATING PREDICTIVE MODELS ON SUPPLEMENTAL WORKBOOK DATA
WO - 26.01.2012
Int.Class G06E 1/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
EOPTICAL COMPUTING DEVICES
1Devices for processing exclusively digital data
 Appl.No PCT/US2011/001250 Applicant PREDIXION SOFTWARE, INC. Inventor MACLENNAN, James, C.
A method and system that generate a predictive model on supplemental workbook data are disclosed. A computer is used to provide a spreadsheet environment comprising data. Supplemental data is defined and stored in a non- worksheet format in the spreadsheet environment. A predictive analytic is performed on the supplemental data. A scalable predictive model is generated in the non-worksheet format. In this way, a greater number of users can create successful predictive analysis projects by providing various tools to create simple and accurate predictive models without requiring extensive training or specific knowledge of the methodologies that are currently required.
7.20190164164COLLABORATIVE 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.

8.20220308943System and AI pattern model for actionable alerts for events within a ChatOps platform
US - 29.09.2022
Int.Class G06F 9/54
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
9Arrangements for program control, e.g. control units
06using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
46Multiprogramming arrangements
54Interprogram communication
 Appl.No 17210853 Applicant KYNDRYL, INC. Inventor Raghuram Srinivasan

In an approach for building a machine learning model that predicts the appropriate action to resolve a malfunction or system error, a processor receives an alert that a malfunction or a system error has occurred. A processor creates a workspace on a ChatOps platform integrated with a chatbot and one or more tools. A processor inputs data relating to the alert in a natural language format. A processor processes the data using a natural language processing algorithm. Responsive to determining a pre-set threshold for outputting the appropriate action is not met, a processor establishes a conversation between two or more support service agents in the workspace. A processor monitors the conversation using the natural language processing algorithm. A processor analyzes a transcript of the conversation using text analytics or pattern matching. A processor creates and trains a machine learning model to predict the appropriate action in future iterations.

9.20130332481Predictive analysis by example
US - 12.12.2013
Int.Class G06F 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
7Methods or arrangements for processing data by operating upon the order or content of the data handled
 Appl.No 13908228 Applicant International Business Machines Corporation Inventor Alex T. Lau

An illustrative embodiment of a computer-implemented method for predictive analytic queries includes creating a user-defined predictive analytics query using a set of syntactic grammar that defines a correct syntax of the user-defined predictive analytics query including a created set of predictive analytics by-example vocabularies and a set of subject-specific by-example vocabularies forming a set of by-example vocabularies, wherein the set of syntactic grammar defines semantics of each syntactically correct predictive analytics query using the by-example vocabularies such that predictive analytics queries can be expressed with semantic precision using this constrained Natural Language Processing (cNLP) approach. The computer-implemented method further generates a predictive analytic model and runtime query, using the user-defined predictive analytics query, executes the runtime query using the predictive analytic model to create a result, and returns the result to the user.

10.20200231466INTELLIGENT SYSTEMS AND METHODS FOR PROCESS AND ASSET HEALTH DIAGNOSIS, ANOMOLY DETECTION AND CONTROL IN WASTEWATER TREATMENT PLANTS OR DRINKING WATER PLANTS
US - 23.07.2020
Int.Class C02F 1/00
CCHEMISTRY; METALLURGY
02TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
FTREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
1Treatment of water, waste water, or sewage
 Appl.No 16472998 Applicant Zijun XIA Inventor Su LU

Described herein are systems and methods of analyzing data acquired from a water plant, both historical and in real-time, making determinations about process and asset health diagnosis and anomaly detection using advanced techniques, and controlling the plant and/or providing alerts based on such determinations.

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