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Analysis

1 / 1,871
1.20220308943System 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.

2.20180139047Cryptographic 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.

3.20200195435Cryptographic 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.

4.20200160997Method for detection and diagnosis of lung and pancreatic cancers from imaging scans
US - 21.05.2020
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
 Appl.No 16673397 Applicant University of Central Florida Research Foundation, Inc. Inventor Ulas Bagci

A method of detecting and diagnosing cancers characterized by the presence of at least one nodule/neoplasm from an imaging scan is presented. To detect nodules in an imaging scan, a 3D CNN using a single feed forward pass of a single network is used. After detection, risk stratification is performed using a supervised or an unsupervised deep learning method to assist in characterizing the detected nodule/neoplasm as benign or malignant. The supervised learning method relies on a 3D CNN used with transfer learning and a graph regularized sparse MTL to determine malignancy. The unsupervised learning method uses clustering to generate labels after which label proportions are used with a novel algorithm to classify malignancy. The method assists radiologists in improving detection rates of lung nodules to facilitate early detection and minimizing errors in diagnosis.

5.11347997Systems and methods using angle-based stochastic gradient descent
US - 31.05.2022
Int.Class G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
04Architecture, e.g. interconnection topology
 Appl.No 17342070 Applicant Chongya Song Inventor Chongya Song

Systems and methods for optimizing and/or solving objective functions are provided. Angle-based stochastic gradient descent (AG-SGD) can be used to alleviate pattern deviation(s) not resolved by related art systems and methods. AG-SGD can use the angle between the current gradient (CG) and the previous gradient (PG) to determine the new gradient (NG).

6.4163833DEEP NEURAL NETWORK MODEL DESIGN ENHANCED BY REAL-TIME PROXY EVALUATION FEEDBACK
EP - 12.04.2023
Int.Class G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
04Architecture, e.g. interconnection topology
 Appl.No 22186944 Applicant INTEL CORP Inventor CUMMINGS DANIEL J
The present disclosure is related to artificial intelligence (AI), machine learning (ML), and Neural Architecture Search (NAS) technologies, and in particular, to Deep Neural Network (DNN) model engineering techniques that use proxy evaluation feedback. The DNN model engineering techniques discussed herein provide near real-time feedback on model performance via low-cost proxy scores without requiring continual training and/or validation cycles, iterations, epochs, etc. In conjunction with the proxy-based scoring, semi-supervised learning mechanisms are used to map proxy scores to various model performance metrics. Other embodiments may be described and/or claimed.
7.20200349229Open domain targeted sentiment classification using semisupervised dynamic generation of feature attributes
US - 05.11.2020
Int.Class G06F 17/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
 Appl.No 16599951 Applicant King Fahd University of Petroleum and Minerals Inventor Shadi Abudalfa

Methods for classification of microblogs using semi-supervised open domain targeted sentiment classification. A hidden Markov model support vector machine (SVM HMM) is trained with a training dataset combined with discrete features. A portion of the training dataset is clustered by k-means clustering to generate cluster IDs which are normalized and combined with the discrete features. After formatting, the combined dataset is applied to the SVM HMM and the C parameter, which is optimized by calculating a zero-one error at each iteration. The open domain targeted sentiment classification methods uses less labelled data than previous sentiment analysis techniques, thus decreasing processing costs. Additionally, a supervised learning model for improving the accuracy of open domain targeted sentiment classification is presented using an SVM HMM.

8.20140188462System and method for analyzing ambiguities in language for natural language processing
US - 03.07.2014
Int.Class G06F 17/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
 Appl.No 14201974 Applicant Zadeh Lotfi A. Inventor Zadeh Lotfi A.

Specification covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), large number of images (“Big Data”) analytics, machine learning, training schemes, crowd-sourcing (using experts or humans), feature space, clustering, classification, similarity measures, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, and data compression.

9.WO/2021/211787SYSTEMS AND METHODS FOR QUANTIFICATION OF LIVER FIBROSIS WITH MRI AND DEEP LEARNING
WO - 21.10.2021
Int.Class G16H 50/20
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
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
20for computer-aided diagnosis, e.g. based on medical expert systems
 Appl.No PCT/US2021/027398 Applicant CHILDREN'S HOSPITAL MEDICAL CENTER Inventor DILLMAN, Jonathan
Embodiments provide a deep learning framework to accurately segment liver and spleen using a convolutional neural network with both short and long residual connections to extract their radiomic and deep features from multiparametric MRI. Embodiments will provide an "ensemble" deep learning model to quantify biopsy derived liver fibrosis stage and percentage using the integration of multiparametric MRI radiomic and deep features, MRE data, as well as routinely available clinical data. Embodiments will provide a deep learning model to quantify MRE-derived liver stiffness using multiparametric MRI, radiomic and deep features and routinely-available clinical data.
10.3937170SPEECH ANALYSIS FOR MONITORING OR DIAGNOSIS OF A HEALTH CONDITION
EP - 12.01.2022
Int.Class G10L 25/66
GPHYSICS
10MUSICAL INSTRUMENTS; ACOUSTICS
LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
25Speech or voice analysis techniques not restricted to a single one of groups G10L15/-G10L21/129
48specially adapted for particular use
51for comparison or discrimination
66for extracting parameters related to health condition
 Appl.No 20185364 Applicant NOVOIC LTD Inventor WESTON JACK
The invention relates to a computer-implemented method of training a machine learning model for performing speech analysis for monitoring or diagnosis of a health condition. The method uses training data comprising audio speech data and comprises obtaining one or more linguistic representations that each encode a sub-word, word, or multiple word sequence, of the audio speech data; obtaining one or more audio representations that each encode audio content of a segment of the audio speech data; combining the linguistic representations and audio representations into an input sequence comprising: linguistic representations of a sequence of one or more words or sub-words of the audio speech data; and audio representations of segments of the audio speech data, where the segments together contain the sequence of the one or more words or sub-words. The method further includes training a machine learning model using unsupervised learning to map the input sequence to a target output to learn combined audio-linguistic representations of the audio speech data for use in speech analysis for monitoring or diagnosis of a health condition.
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