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Analysis

1 / 854
1.20230179955DYNAMIC AND ADAPTIVE SYSTEMS AND METHODS FOR REWARDING AND/OR DISINCENTIVIZING BEHAVIORS
US - 08.06.2023
Int.Class H04W 4/029
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
WWIRELESS COMMUNICATION NETWORKS
4Services specially adapted for wireless communication networks; Facilities therefor
02Services making use of location information
029Location-based management or tracking services
 Appl.No 18090047 Applicant Conquer Your Addiction LLC Inventor David H. Williams

The present disclosure generally relates to dynamic and adaptive systems and methods for rewarding and/or disincentivizing behaviors.

2.20250016520SYSTEMS AND METHODS FOR THE USE OF DIGITAL AGENTS
US - 09.01.2025
Int.Class H04W 4/021
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
WWIRELESS COMMUNICATION NETWORKS
4Services specially adapted for wireless communication networks; Facilities therefor
02Services making use of location information
021Services related to particular areas, e.g. point of interest services, venue services or geofences
 Appl.No 18885300 Applicant Conquer Your Addiction LLC Inventor David H. Williams

The present disclosure generally relates to systems and methods for the use of digital agents. In exemplary embodiments, a system is configured to be operable for creating, developing, formulating, programming, informing, educating, influencing, facilitating, modifying, controlling, directing, understanding, using, and/or learning from/with/using one of more digital construct(s) of one or more entity(ies) and/or group(s) that are based and/or focused on one or more motivation(s), ethic(s), moral(s), reputation(s), and/or their root cause(s) of the one or more entity(ies) and/or group(s). The one or more digital construct(s) are configured to have a primary function of providing information about the one or more motivation(s), ethic(s), moral(s), reputation(s), and/or their root cause(s) of the one or more entity(ies) and/or group(s) to the one or more digital construct(s) and/or to another digital construct(s), system(s), device(s), sensor(s), sensor array(s), and/or network(s).

3.20250016521SYSTEMS AND METHODS FOR TRIGGER RISK, SUBSTANCE USE, AND/OR UNDESIRABLE BEHAVIOR DETECTION
US - 09.01.2025
Int.Class H04W 4/021
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
WWIRELESS COMMUNICATION NETWORKS
4Services specially adapted for wireless communication networks; Facilities therefor
02Services making use of location information
021Services related to particular areas, e.g. point of interest services, venue services or geofences
 Appl.No 18885307 Applicant Conquer Your Addiction LLC Inventor David H. WILLIAMS

The present disclosure generally relates to systems and methods for trigger risk, substance use, and/or undesirable behavior detection. In exemplary embodiments, a system is configured to be operable for identifying trigger risk(s), undesirable and/or desirable behavior(s) and/or substance (ab)use and/or addictive activity(ies) of one or more entity(ies) and determining and implementing action(s) or pre-empting and/or mitigating undesirable behavior(s) and/or encouraging and/or facilitating desirable behavior(s). The system comprises a plurality of digital and/or physical constructs and/or engines operable for providing various capabilities. The system is configured to be operable for providing a plurality of data from sensor(s), sensor array(s), device(s), system(s), and/or network(s) to the plurality of digital and/or physical constructs and/or engines.

4.2017300259Distributed 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.
5.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.
6.3031067DISTRIBUTED MACHINE LEARNING SYSTEMS, APPARATUS, AND METHODS
CA - 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 3031067 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.
7.20220405644Distributed machine learning systems, apparatus, and methods
US - 22.12.2022
Int.Class G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
 Appl.No 17890953 Applicant NantOmics, LLC Inventor Christopher W. Szeto

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.

8.20180018757TRANSFORMING PROJECTION DATA IN TOMOGRAPHY BY MEANS OF MACHINE LEARNING
US - 18.01.2018
Int.Class G06T 3/40
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
3Geometric image transformations in the plane of the image
40Scaling of whole images or parts thereof, e.g. expanding or contracting
 Appl.No 15646119 Applicant Kenji SUZUKI Inventor Kenji SUZUKI

A method and system for transforming low-quality projection data into higher quality projection data, using of a machine learning model. Regions are extracted from an input projection image acquired, for example, at a reduced x-ray radiation dose (lower-dose), and pixel values in the region are entered into the machine learning model as input. The output of the machine learning model is a region that corresponds to the input region. The output information is arranged to form an output high-quality projection image. A reconstruction algorithm reconstructs high-quality tomographic images from the output high-quality projection images. The machine learning model is trained with matched pairs of projection images, namely, input lower-quality (lower-dose) projection images together with corresponding desired higher-quality (higher-dose) projection images. Through the training, the machine learning model learns to transform lower-quality (lower-dose) projection images to higher-quality (higher-dose) projection images. Once trained, the trained machine learning model does not require the higher-quality (higher-dose) projection images anymore. When a new lower-quality (low radiation dose) projection image is entered, the trained machine learning model would output a region similar to its desired region, in other words, it would output simulated high-quality (high-dose) projection images where noise and artifacts due to low radiation dose are substantially reduced, i.e., a higher image quality. The reconstruction algorithm reconstructs simulated high-quality (high-dose) tomographic images from the output high-quality (high-dose) projection images. With the simulated high-quality (high-dose) tomographic images, the detectability of lesions and clinically important findings can be improved.

9.20180196942Endpoint detection and response utilizing machine learning
US - 12.07.2018
Int.Class G06F 21/56
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
21Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
50Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
55Detecting local intrusion or implementing counter-measures
56Computer malware detection or handling, e.g. anti-virus arrangements
 Appl.No 15862067 Applicant Cylance Inc. Inventor Rahul Chander Kashyap

A plurality of events associated with each of a plurality of computing nodes that form part of a network topology are monitored. The network topology includes antivirus tools to detect malicious software prior to it accessing one of the computing nodes. Thereafter, it is determined that, using at least one machine learning model, at least one of the events is indicative of malicious activity that has circumvented or bypassed the antivirus tools. Data is then provided that characterizes the determination. Related apparatus, systems, techniques and articles are also described.

10.20160155069Machine learning classifier that can determine classifications of high-risk items
US - 02.06.2016
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 15014773 Applicant Accenture Global Solutions Limited Inventor James Hoover

A machine learning classifier system includes a data set processing subsystem to generate a training set and a validation set from multiple data sources. Classifier hardware induces a classifier according to the training set, and tests the classifier according to the validation set. A buffer connected to the classifier hardware stores data objects to be classified, and a register connected to the classifier hardware stores outputs of the classifier, including classified data objects.

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