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Analysis

1 / 2,296
1.12274503Myopia 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.

2.08527432Semi-supervised learning based on semiparametric regularization
US - 03.09.2013
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 12538849 Applicant Guo Zhen Inventor Guo Zhen

Semi-supervised learning plays an important role in machine learning and data mining. The semi-supervised learning problem is approached by developing semiparametric regularization, which attempts to discover the marginal distribution of the data to learn the parametric function through exploiting the geometric distribution of the data. This learned parametric function can then be incorporated into the supervised learning on the available labeled data as the prior knowledge. A semi-supervised learning approach is provided which incorporates the unlabeled data into the supervised learning by a parametric function learned from the whole data including the labeled and unlabeled data. The parametric function reflects the geometric structure of the marginal distribution of the data. Furthermore, the proposed approach which naturally extends to the out-of-sample data is an inductive learning method in nature.

3.20160329044Semi-supervised learning of word embeddings
US - 10.11.2016
Int.Class G06F 17/27
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
20Handling natural language data
27Automatic analysis, e.g. parsing, orthograph correction
 Appl.No 14707720 Applicant International Business Machines Corporation Inventor Liangliang Cao

Software that trains an artificial neural network for generating vector representations for natural language text, by performing the following steps: (i) receiving, by one or more processors, a set of natural language text; (ii) generating, by one or more processors, a set of first metadata for the set of natural language text, where the first metadata is generated using supervised learning method(s); (iii) generating, by one or more processors, a set of second metadata for the set of natural language text, where the second metadata is generated using unsupervised learning method(s); and (iv) training, by one or more processors, an artificial neural network adapted to generate vector representations for natural language text, where the training is based, at least in part, on the received natural language text, the generated set of first metadata, and the generated set of second metadata.

4.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.
5.20170162189Semi-supervised learning of word embeddings
US - 08.06.2017
Int.Class G06F 17/27
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
20Handling natural language data
27Automatic analysis, e.g. parsing, orthograph correction
 Appl.No 15437490 Applicant International Business Machines Corporation Inventor Liangliang Cao

Software that trains an artificial neural network for generating vector representations for natural language text, by performing the following steps: (i) receiving, by one or more processors, a set of natural language text; (ii) generating, by one or more processors, a set of first metadata for the set of natural language text, where the first metadata is generated using supervised learning method(s); (iii) generating, by one or more processors, a set of second metadata for the set of natural language text, where the second metadata is generated using unsupervised learning method(s); and (iv) training, by one or more processors, an artificial neural network adapted to generate vector representations for natural language text, where the training is based, at least in part, on the received natural language text, the generated set of first metadata, and the generated set of second metadata.

6.20160328388Semi-supervised learning of word embeddings
US - 10.11.2016
Int.Class G06F 17/27
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
20Handling natural language data
27Automatic analysis, e.g. parsing, orthograph correction
 Appl.No 14870204 Applicant International Business Machines Corporation Inventor Liangliang Cao

Software that trains an artificial neural network for generating vector representations for natural language text, by performing the following steps: (i) receiving, by one or more processors, a set of natural language text; (ii) generating, by one or more processors, a set of first metadata for the set of natural language text, where the first metadata is generated using supervised learning method(s); (iii) generating, by one or more processors, a set of second metadata for the set of natural language text, where the second metadata is generated using unsupervised learning method(s); and (iv) training, by one or more processors, an artificial neural network adapted to generate vector representations for natural language text, where the training is based, at least in part, on the received natural language text, the generated set of first metadata, and the generated set of second metadata.

7.20250053832Systems, Methods and Apparatus for Machine Learning Predictive Analytics
US - 13.02.2025
Int.Class G06N 5/022
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computing arrangements using knowledge-based models
02Knowledge representation; Symbolic representation
022Knowledge engineering; Knowledge acquisition
 Appl.No 18448966 Applicant Ryan Newsome Inventor Ryan Newsome

Systems, methods and apparatus of machine-learning-predictive-analytics, the method performed by a predictive analytic control computer and including receiving from a second computer a training-profile data that describes one or more contributions of resources that are associated and identified with particular entities, receiving from a third computer a training-profile data that are associated and identified with the particular entities, that does not describe one or more contributions of resources the training-profile data, and that includes data that is that is received from additional computers that host websites and applications that focus on communication, community-based input, interaction, content-sharing and collaboration that describe a first set of features and representations of issues of interest of the particular entities, generating a machine-learning-predictive-analytic model by a machine learning-predictive-analytic trainer in reference to the training-profile data, generating predictions from the machine-learning-predictive-analytic model and from a second set of features and representations of issues of interest.

8.20230196117TRAINING METHOD FOR SEMI-SUPERVISED LEARNING MODEL, IMAGE PROCESSING METHOD, AND DEVICE
US - 22.06.2023
Int.Class G06N 3/0895
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
0895Weakly supervised learning, e.g. semi-supervised or self-supervised learning
 Appl.No 18173310 Applicant HUAWEI TECHNOLOGIES CO., LTD. Inventor Zewei DU

Embodiments of this application disclose a training method for a semi-supervised learning model which can be applied to computer vision in the field of artificial intelligence. The method includes: first predicting classification categories of some unlabeled samples by using a trained first semi-supervised learning model, to obtain a prediction label; and determining whether each prediction label is correct in a one-bit labeling manner, and if prediction is correct, obtaining a correct label (a positive label) of the sample, or if prediction is incorrect, excluding an incorrect label (a negative label) of the sample. Then, in a next training phase, a training set (a first training set) is reconstructed based on the information, and an initial semi-supervised learning model is retrained based on the first training set, to improve prediction accuracy of the model. In one-bit labeling, an annotator only needs to answer “yes” or “no” for the prediction label.

9.20190164164COLLABORATIVE 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.

10.20060106797System and method for temporal data mining
US - 18.05.2006
Int.Class G06E 1/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
EOPTICAL COMPUTING DEVICES
1Devices for processing exclusively digital data
 Appl.No 11199698 Applicant GM Global Technology Operations, Inc. Inventor Srinivasa Narayan

A system, method, and apparatus for signal characterization, estimation, and prediction comprising an integrated search algorithm that cooperatively optimizes several data mining sub-tasks, the integrated search algorithm including a machine learning model, and the method comprising processing the data for data embedding, data embedding the processed data for searching for patterns, extracting time and frequency patterns, and training the model to represent learned patterns for signal characterization, estimation, and prediction.

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