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Analysis

1 / 444
1.20200342307Swarm 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.

2.20140180975INSTANCE WEIGHTED LEARNING MACHINE LEARNING MODEL
US - 26.06.2014
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 13725653 Applicant INSIDESALES.COM, INC. Inventor Martinez Tony Ramon

An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.

3.2013364041Instance weighted learning machine learning model
AU - 09.07.2015
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 2013364041 Applicant InsideSales.com, Inc. Inventor Martinez, Tony Ramon
An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.
4.WO/2024/054286MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING (NLP)-BASED SYSTEM FOR SYSTEM-ON-CHIP (SOC) TROUBLESHOOTING
WO - 14.03.2024
Int.Class G06F 30/33
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
30Computer-aided design
30Circuit design
32Circuit design at the digital level
33Design verification, e.g. functional simulation or model checking
 Appl.No PCT/US2023/026170 Applicant QUALCOMM INCORPORATED Inventor CAKIR, Murat
A method for processor-implemented method includes receiving an integrated circuit (IC) troubleshooting query for an IC (816). The IC troubleshooting query (816) is received from a user. The method also includes performing natural language processing and machine learning to cluster the IC troubleshooting query into one of a number of semantically similar troubleshooting categories. The method further includes retrieving resolution data from an expert system library (812), based on a mapping between categories of user solutions and a topic of the IC troubleshooting query. The method also includes generating a recommendation in response to the IC troubleshooting query, based on the resolution data (818). The method outputs the recommendation to the user.
5.20210287664Machine learning used to detect alignment and misalignment in conversation
US - 16.09.2021
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 16817944 Applicant Palo Alto Research Center Incorporated Inventor Evgeniy Bart

Digitized media is received that records a conversation between individuals. Cues are extracted from the digitized media that indicate properties of the conversation. The cues are entered as training data into a machine learning module to create a trained machine learning model. The trained machine learning model is used in a processor to detect other misalignments in subsequent digitized conversations.

6.20140052678Hierarchical based sequencing machine learning model
US - 20.02.2014
Int.Class G06E 1/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
EOPTICAL COMPUTING DEVICES
1Devices for processing exclusively digital data
 Appl.No 13590000 Applicant Martinez Tony Ramon Inventor Martinez Tony Ramon

A hierarchical based sequencing (HBS) machine learning model. In one example embodiment, a method of employing an HBS machine learning model to predict multiple interdependent output components of an MOD output decision may include determining an order for multiple interdependent output components of an MOD output decision. The method may also include sequentially training a classifier for each component in the selected order to predict the component based on an input and based on any previous predicted component(s).

7.WO/2014/100738INSTANCE WEIGHTED LEARNING MACHINE LEARNING MODEL
WO - 26.06.2014
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/US2013/077260 Applicant INSIDESALES.COM, INC. Inventor MARTINEZ, Tony, Ramon
An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.
8.20140180978INSTANCE WEIGHTED LEARNING MACHINE LEARNING MODEL
US - 26.06.2014
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 14189669 Applicant INSIDESALES.COM, INC. Inventor Martinez Tony Ramon

An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model may include identifying a temporal sequence of reinforcement learning machine learning training instances with each of the training instances including a state-action pair, determining a first quality value for a first training instance in the temporal sequence of reinforcement learning machine learning training instances determining a second quality value for a second training instance in the temporal sequence of reinforcement learning machine learning training instances, associating the first quality value with the first training instance, and associating the second quality value with the second training instance. In this example embodiment, the first quality value is higher than the second quality value.

9.09661163Machine learning based system and method for improving false alert triggering in web based device management applications
US - 23.05.2017
Int.Class G06F 3/12
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
3Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
12Digital output to print unit
 Appl.No 15042626 Applicant Xerox Corporation Inventor Helen Haekyung Shin

Methods, systems, and processor-readable media for remotely providing a device status alert. In an example embodiment, data indicative of the status of one or more devices can be subject to an HMM (Hidden Markov Model) and a dynamic programming algorithm to determine the latent state of the device (or devices). A status alert model can be trained based on such data and can be expanded with respect to a wide range of devices including utilizing semi-supervised learning. The alert status model can then be integrated into a device management application that provides a status alert regarding one or more of such devices based on the status alert model.

10.WO/2014/031683HIERARCHICAL BASED SEQUENCING MACHINE LEARNING MODEL
WO - 27.02.2014
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/US2013/055856 Applicant INSIDESALES.COM, INC. Inventor MARTINEZ, Tony, Ramon
A hierarchical based sequencing (HBS) machine learning model. In one example embodiment, a method of employing an HBS machine learning model to predict multiple interdependent output components of an MOD output decision may include determining an order for multiple interdependent output components of an MOD output decision. The method may also include sequentially training a classifier for each component in the selected order to predict the component based on an input and based on any previous predicted component(s).
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