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Analysis

1 / 1,405
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.

2.20060106797System 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.

3.20190171948Computing architecture deployment configuration recommendation using machine learning
US - 06.06.2019
Int.Class G06N 5/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computing arrangements using knowledge-based models
04Inference or reasoning models
 Appl.No 15829717 Applicant SAP SE Inventor Renjith Pillai

Data is received that characterizes a software system. Thereafter, using at least one machine learning model trained using historical testing data from a plurality of training software systems, a recommended computing architecture is generated for the software system. Data can then be provided that characterizes the software system. Related apparatus, systems, techniques and articles are also described.

4.WO/2018/017467DISTRIBUTED MACHINE LEARNING SYSTEMS, APPARATUS, AND METHODS
WO - 25.01.2018
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 PCT/US2017/042356 Applicant NANTOMICS, LLC Inventor SZETO, Christopher
A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.
5.20200294331Apparatus and method for predicting injury level
US - 17.09.2020
Int.Class G07C 5/08
GPHYSICS
07CHECKING-DEVICES
CTIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
5Registering or indicating the working of vehicles
08Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
 Appl.No 16521071 Applicant Hyundai Motor Company Inventor Kyu Sang Choi

An apparatus for predicting an injury level of a user of a vehicle may include: a communication circuit configured to communicate with an external device; a memory configured to store a genetic algorithm and a machine learning model; and a processor electrically connected with the communication circuit and the memory. The processor may be configured to: obtain, via the communication circuit, traffic accident data associated with a traffic accident; select input data, which includes at least a part of the traffic accident data, for training of the machine learning model, the input data selected using the genetic algorithm; train the machine learning model using the input data; and predict an injury level of the user of the vehicle using the trained machine learning model when the training of the machine learning model is completed.

6.2017300259Distributed machine learning systems, apparatus, and methods
AU - 25.01.2018
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 2017300259 Applicant Nant Holdings IP, LLC Inventor Benz, Stephen Charles
A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.
7.20180139047Cryptographic key control based on debasing condition likelihood estimation
US - 17.05.2018
Int.Class H04L 9/14
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
9Arrangements for secret or secure communications; Network security protocols
14using a plurality of keys or algorithms
 Appl.No 15236043 Applicant Venafi, Inc. Inventor Matthew Woods

In representative embodiments, systems and methods to calculate the likelihood that presented cryptographic key material is untrustworthy are disclosed. A predictive model based on a debasing condition and a dataset is created by evaluating the dataset relative to the debasing condition. For example, if certificate revocation is selected as the debasing condition, the dataset is analyzed to produce a predictive model that determines the likelihood that a presented certificate is untrustworthy based on similarity to already revoked certificates. The predictive model can include a supervised learning model like a logistic regression model or a deep neural network model. The system can be used in conjunction with existing security infrastructures or can be used as a separate infrastructure. Based on the likelihood score calculated by the model, a relying system can reject the cryptographic key material, accept the cryptographic key material or take other further action.

8.10430946Medical image segmentation and severity grading using neural network architectures with semi-supervised learning techniques
US - 01.10.2019
Int.Class G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
 Appl.No 16353800 Applicant Inception Institute of Artifical Intelligence, Ltd. Inventor Yi Zhou

This disclosure relates to improved techniques for performing computer vision functions on medical images, including object segmentation functions for identifying medical objects in the medical images and grading functions for determining severity labels for medical conditions exhibited in the medical images. The techniques described herein utilize a neural network architecture to perform these and other functions. The neural network architecture can be trained, at least in part, using semi-supervised learning techniques that enable the neural network architecture to accurately perform the object segmentation and grading functions despite limited availability of pixel-level annotation information.

9.20200195435Cryptographic key control based on debasing condition likelihood estimation
US - 18.06.2020
Int.Class H04L 9/14
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
9Arrangements for secret or secure communications; Network security protocols
14using a plurality of keys or algorithms
 Appl.No 16692319 Applicant Venafi, Inc. Inventor Matthew Woods

In representative embodiments, systems and methods to calculate the likelihood that presented cryptographic key material is untrustworthy are disclosed. A predictive model based on a debasing condition and a dataset is created by evaluating the dataset relative to the debasing condition. For example, if certificate revocation is selected as the debasing condition, the dataset is analyzed to produce a predictive model that determines the likelihood that a presented certificate is untrustworthy based on similarity to already revoked certificates. The predictive model can include a supervised learning model like a logistic regression model or a deep neural network model. The system can be used in conjunction with existing security infrastructures or can be used as a separate infrastructure. Based on the likelihood score calculated by the model, a relying system can reject the cryptographic key material, accept the cryptographic key material or take other further action.

10.WO/2020/183230MEDICAL IMAGE SEGMENTATION AND SEVERITY GRADING USING NEURAL NETWORK ARCHITECTURES WITH SEMI-SUPERVISED LEARNING TECHNIQUES
WO - 17.09.2020
Int.Class G06T 7/10
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
10Segmentation; Edge detection
 Appl.No PCT/IB2019/056609 Applicant INCEPTION INSTITUTE OF ARTIFICIAL INTELLIGENCE, LTD. Inventor ZHOU, Yi
This disclosure relates to improved techniques for performing computer vision functions on medical images, including object segmentation functions for identifying medical objects in the medical images and grading functions for determining severity labels for medical conditions exhibited in the medical images. The techniques described herein utilize a neural network architecture to perform these and other functions. The neural network architecture can be trained, at least in part, using semi-supervised learning techniques that enable the neural network architecture to accurately perform the object segmentation and grading functions despite limited availability of pixel-level annotation information.
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