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Analysis

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.

2.20210141635Intelligent software agent to facilitate software development and operations
US 13.05.2021
Int.Class G06F 8/70
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
8Arrangements for software engineering
70Software maintenance or management
Appl.No 17154184 Applicant Hartford Fire Insurance Company Inventor Renoi Thomas

Some embodiments may facilitate software development and operations for an enterprise. A communication input port may receive information associated with a software continuous integration/deployment pipeline of the enterprise. An intelligent software agent platform, coupled to the communication input port, may listen for a trigger indication from the software continuous integration/deployment pipeline. Responsive to the trigger indication, the intelligent software agent platform may apply system configuration information and rule layer information to extract software log data and apply a machine learning model to the extracted software log data to generate a pipeline health check analysis report. The pipeline health check analysis report may include, for example, an automatically generated prediction associated with future operation of the software continuous integration/deployment pipeline. The intelligent software agent platform may then facilitate transmission of the pipeline health check analysis report via a communication output port and a distributed communication network.

3.20200401397Intelligent software agent to facilitate software development and operations
US 24.12.2020
Int.Class G06F 8/70
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
8Arrangements for software engineering
70Software maintenance or management
Appl.No 16450104 Applicant Hartford Fire Insurance Company Inventor Renoi Thomas, Jr.

Some embodiments may facilitate software development and operations for an enterprise. A communication input port may receive information associated with a software continuous integration/deployment pipeline of the enterprise. An intelligent software agent platform, coupled to the communication input port, may listen for a trigger indication from the software continuous integration/deployment pipeline. Responsive to the trigger indication, the intelligent software agent platform may apply system configuration and rule layer information to extract software log data and apply a machine learning model to the extracted software log data to generate a pipeline health check analysis report. The pipeline health check analysis report may include, for example, an automatically generated prediction associated with future operation of the software continuous integration/deployment pipeline. The intelligent software agent platform may then facilitate transmission of the pipeline health check analysis report via a communication output port and a distributed communication network.

4.20230009897Intelligent software agent to facilitate software development and operations
US 12.01.2023
Int.Class G06F 8/70
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
8Arrangements for software engineering
70Software maintenance or management
Appl.No 17948785 Applicant Hartford Fire Insurance Company Inventor Renoi Thomas

Some embodiments may facilitate software development and operations for an enterprise. A communication input port may receive information associated with a software continuous integration/deployment pipeline of the enterprise. An intelligent software agent platform, coupled to the communication input port, may listen for a trigger indication from the software continuous integration/deployment pipeline. Responsive to the trigger indication, the intelligent software agent platform may apply system configuration information and rule layer information to extract software log data and apply a machine learning model to the extracted software log data to generate a pipeline health check analysis report. The pipeline health check analysis report may include, for example, an automatically generated prediction associated with future operation of the software continuous integration/deployment pipeline. The intelligent software agent platform may then facilitate transmission of the pipeline health check analysis report via a communication output port and a distributed communication network.

5.20200078679Machine learning models for implementing animation actions
US 12.03.2020
Int.Class A63F 9/00
AHUMAN NECESSITIES
63SPORTS; GAMES; AMUSEMENTS
FCARD, BOARD OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
9Games not otherwise provided for
Appl.No 16125288 Applicant Electronic Arts, Inc. Inventor Cesar Dantas de Castro

An animation system and method generates predictive models that are deployed in animations, such as an animation associated with a video game, to predict outcomes resulting from events occurring in the animation, such as interactions between two or more objects in the animation. These predictive models may be generated based at least in part on training data that is generated by running the animation, such as playing a video game, generating parameter values associated with events in the animation, and determining an outcome of the events. The training data may be used to generate predictive models, such as by using machine learning algorithms. The predictive models may then be deployed in the animation, such as in a video game, to make real time or near real-time predictions of outcomes based at least in part on events in the animation.

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.
7.20210183392PHONEME-BASED NATURAL LANGUAGE PROCESSING
US 17.06.2021
Int.Class G10L 15/26
GPHYSICS
10MUSICAL INSTRUMENTS; ACOUSTICS
LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
15Speech recognition
26Speech to text systems
Appl.No 17028361 Applicant LG ELECTRONICS INC. Inventor Kwangyong LEE

A natural language processing method and apparatus are disclosed. A natural language processing method according to an embodiment of the present disclosure includes extracting a phoneme string from a text corpus labeled with recognition information including at least one of one named entity (NE) or speech intention, generating a phoneme-based training data set by labeling the recognition information in the extracted phoneme string, and generating an artificial neural network-based learning model (LM) using the generated training data set. The natural language processing method of the present disclosure may be associated with an artificial intelligence module, a drone (Unmanned Aerial Vehicle, UAV), a robot, an AR (Augmented Reality) device, a VR (Virtual Reality) device, a device associated with 5G services, etc.

8.WO/2018/226492ASYNCHRONOUS AGENTS WITH LEARNING COACHES AND STRUCTURALLY MODIFYING DEEP NEURAL NETWORKS WITHOUT PERFORMANCE DEGRADATION
WO 13.12.2018
Int.Class G06N 3/02
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
Appl.No PCT/US2018/035275 Applicant D5AI LLC Inventor BAKER, James K.
Methods and computer systems improve a trained base deep neural network by structurally changing the base deep neural network to create an updated deep neural network, such that the updated deep neural network has no degradation in performance relative to the base deep neural network on the training data. The updated deep neural network is subsequently training. Also, an asynchronous agent for use in a machine learning system comprises a second machine learning system ML2 that is to be trained to perform some machine learning task. The asynchronous agent further comprises a learning coach LC and an optional data selector machine learning system DS. The purpose of the data selection machine learning system DS is to make the second stage machine learning system ML2 more efficient in its learning (by selecting a set of training data that is smaller but sufficient) and/or more effective (by selecting a set of training data that is focused on an important task). The learning coach LC is a machine learning system that assists the learning of the DS and ML2. Multiple asynchronous agents could also be in communication with each others, each trained and grown asynchronously under the guidance of their respective learning coaches to perform different tasks.
9.WO/2020/069517INTELLIGENT TRANSPORTATION SYSTEMS
WO 02.04.2020
Int.Class B60W 30/14
BPERFORMING OPERATIONS; TRANSPORTING
60VEHICLES IN GENERAL
WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
30Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
14Cruise control
Appl.No PCT/US2019/053857 Applicant STRONG FORCE INTELLECTUAL CAPITAL, LLC Inventor CELLA, Charles Howard
Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.
10.3143234INTELLIGENT TRANSPORTATION SYSTEMS
CA 02.04.2020
Int.Class B60W 20/12
BPERFORMING OPERATIONS; TRANSPORTING
60VEHICLES IN GENERAL
WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
20Control systems specially adapted for hybrid vehicles
10Controlling the power contribution of each of the prime movers to meet required power demand
12using control strategies taking into account route information
Appl.No 3143234 Applicant STRONG FORCE INTELLECTUAL CAPITAL, LLC Inventor CELLA, CHARLES HOWARD
Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.