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Analysis

1 / 3,762
1.10990645System and methods for performing automatic data aggregation
US - 27.04.2021
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 16127764 Applicant Sophtron, Inc. Inventor Nanjuan Shi

Systems, apparatuses, and methods for automated data aggregation. In some embodiments, this is achieved by use of techniques such as natural language processing (NLP) and machine learning to enable the automation of data aggregation from websites without the use of pre-programmed scripts.

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.

3.11526579System and methods for performing automatic data aggregation
US - 13.12.2022
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 17212316 Applicant Sophtron, Inc. Inventor Nanjuan Shi

Systems, apparatuses, and methods for automated data aggregation, automated webpage navigation, or automatically performing a task by entering data into multiple webpages. In some embodiments, this is achieved by use of techniques such as natural language processing (NLP) and machine learning to enable the automation of data aggregation and other tasks involving websites without the use of pre-programmed scripts.

4.20240333578REAL-TIME MONITORING ECOSYSTEM
US - 03.10.2024
Int.Class H04L 41/0631
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
41Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
06Management of faults, events, alarms or notifications
0631using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
 Appl.No 18129306 Applicant CITIBANK, N.A. Inventor Japan Mehta

A network system to provide real-time integration and processing of user data with infrastructure data to generate solutions to user pain points. Real-time user data, including feedback and interactions, is generally not uniform and overwhelmingly large. The system provides solutions to user pain-points at scale, which, in some instances, may be unknown to the service provider. The system does so by contextually linking user data and categorizing it into standardized taxonomies. The infrastructure data is then analyzed against the taxonomies by the system's AI/ML network. The system then provides one or more pain point identifications and solutions. The system may also provide an interface to visualize the taxonomies, pain points, and trend analysis of the pain points.

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.

6.20220139498APPARATUSES, SYSTEMS, AND METHODS FOR EXTRACTING MEANING FROM DNA SEQUENCE DATA USING NATURAL LANGUAGE PROCESSING (NLP)
US - 05.05.2022
Int.Class G16B 40/00
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
 Appl.No 17088734 Applicant BASF CORPORATION Inventor Erin Marie Davis

Apparatuses, systems, and methods are provided that may analyze deoxyribonucleic add (DNA) sequence data using a natural language processing (NLP) model to, for example, identify genetic elements such as known and/or novel cis-regulatory elements (e.g., known and/or putative novel drought-responsive cis-regulatory elements (DREs)). Apparatuses, systems, and methods are also provided that may identify transcriptional regulators (e.g., upstream transcriptional regulators of a novel putative DRE) based on natural language processing (NLP) model data and expression genome-wide association study (eGWAS) data. Apparatuses, systems, and methods are also provided that may verify putative novel cis-regulatory elements based on a comparison of natural language processing (NLP) model output data and other model output data.

7.20240071569APPARATUSES, SYSTEMS, AND METHODS FOR EXTRACTING MEANING FROM DNA SEQUENCE DATA USING NATURAL LANGUAGE PROCESSING (NLP)
US - 29.02.2024
Int.Class G16B 40/00
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
 Appl.No 18034417 Applicant BASF CORPORATION Inventor Erin Marie Davis

Apparatuses, systems, and methods are provided that may analyze deoxyribonucleic add (DNA) sequence data using a natural language processing (NLP) model to, for example, identify genetic elements such as known and/or novel cis-regulatory elements {e.g., known and/or putative novel drought-responsive cis-regulatory elements (DREs)). Apparatuses, systems, and methods are also provided that may identify transcriptional regulators {e.g., upstream transcriptional regulators of a novel putative DRE) based on natural language processing (NLP) model data and expression genome-wide association study (eGWAS) data. Apparatuses, systems, and methods are also provided that may verify putative novel cis-regulatory elements based on a comparison of natural language processing (NLP) model output data and other model output data.

8.WO/2019/113122SYSTEMS AND METHODS FOR IMPROVED MACHINE LEARNING FOR CONVERSATIONS
WO - 13.06.2019
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 PCT/US2018/063928 Applicant CONVERSICA, INC. Inventor TERRY, George, Alexis
Systems and methods for improvements in AI model learning and updating are provided. The model updating may reuse existing business conversations as the training data set. Features within the dataset may be defined and extracted. Models may be selected and parameters for the models defined. Within a distributed computing setting the parameters may be optimized, and the models deployed. The training data may be augmented over time to improve the models. Deep learning models may be employed to improve system accuracy, as can active learning techniques. The models developed and updated may be employed by a response system generally, or may function to enable specific types of AI systems. One such a system may be an AI assistant that is designed to take use cases and objectives, and execute tasks until the objectives are met. Another system capable of leveraging the models includes an automated question answering system utilizing approved answers. Yet another system for utilizing these various classification models is an intent based classification system for action determination. Lastly, it should be noted that any of the above systems may be further enhanced by enabling multiple language analysis.
9.20230132247APPARATUS AND METHODS FOR MACHINE LEARNING TO IDENTIFY AND DIAGNOSE INTRACRANIAL HEMORRHAGES
US - 27.04.2023
Int.Class A61B 5/02
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
02Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
 Appl.No 17508993 Applicant Benjamin Steven Hopkins Inventor Benjamin Steven Hopkins

In some embodiments, an apparatus includes providing a representation of a set of digital medical images to a first machine learning model to define a feature vector associated with a presence of an intracranial hemorrhage. A representation of the set of digital medical images is provided to a second machine learning model to define a second feature vector associated with a volume of the intracranial hemorrhage. Using a third machine learning model, a set of EMRs associated with risk factors for a predefined indication is analyzed to define a third feature vector. The first, second and third feature vectors are provided as inputs to a fourth machine learning model to determine a metric associated with an applicability of a specific treatment associated with a predefined indication. An alert is sent to relevant healthcare providers and relevant tests, procedures or bloodwork are ordered for the predefined indication.

10.20200342307Swarm fair deep reinforcement learning
US - 29.10.2020
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 16395187 Applicant International Business Machines Corporation Inventor Aaron K. Baughman

Fair deep reinforcement learning is provided. A microstate of an environment and reaction of items in a plurality of microstates within the environment are observed after an agent performs an action in the environment. Semi-supervised training is utilized to determine bias weights corresponding to the action for the microstate of the environment and the reaction of the items in the plurality of microstates within the environment. The bias weights from the semi-supervised training are merged with non-bias weights using an artificial neural network. Over time, it is determined where bias is occurring in the semi-supervised training based on merging the bias weights with the non-bias weights in the artificial neural network. A deep reinforcement learning model that decreases reliance on the bias weights is generated based on determined bias to increase fairness.

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