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Analysis

1 / 931
1.20200272947Orchestrator for machine learning pipeline
US - 27.08.2020
Int.Class G06F 15/173
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
15Digital computers in general; Data processing equipment in general
16Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
163Interprocessor communication
173using an interconnection network, e.g. matrix, shuffle, pyramid, star or snowflake
 Appl.No 16284291 Applicant SAP SE Inventor Lukas Carullo

Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

2.20210397895INTELLIGENT LEARNING SYSTEM WITH NOISY LABEL DATA
US - 23.12.2021
Int.Class G06K 9/62
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KGRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for recognising patterns
62Methods or arrangements for pattern recognition using electronic means
 Appl.No 16946465 Applicant INTERNATIONAL BUSINESS MACHINES CORPORATION Inventor Yang SUN

Various embodiments are provided for providing machine learning with noisy label data in a computing environment using one or more processors in a computing system. A label corruption probability of noisy labels may be estimated for selected data from a dataset using temporal inconsistency in a machine model prediction during a training operation in a neural network.

3.20230206137Orchestrator for machine learning pipeline
US - 29.06.2023
Int.Class G06F 15/173
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
15Digital computers in general; Data processing equipment in general
16Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
163Interprocessor communication
173using an interconnection network, e.g. matrix, shuffle, pyramid, star or snowflake
 Appl.No 18111839 Applicant SAP SE Inventor Lukas Carullo

Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

4.10990645System 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.

5.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.
6.20190056715Framework for rapid additive design with generative techniques
US - 21.02.2019
Int.Class G05B 19/4099
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
19Programme-control systems
02electric
18Numerical control , i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
4097characterised by using design data to control NC machines, e.g. CAD/CAM
4099Surface or curve machining, making 3D objects, e.g. desktop manufacturing
 Appl.No 15678653 Applicant General Electric Company Inventor Arun Karthi Subramaniyan

According to some embodiments, a system may include a design experience data store containing electronic records associated with prior industrial asset item designs. A deep learning model platform, coupled to the design experience data store, may include a communication port to receive constraint and load information from a designer device. The deep learning platform may further include a computer processor adapted to automatically and generatively create boundaries and geometries, using a deep learning model associated with an additive manufacturing process, for an industrial asset item based on the prior industrial asset item designs and the received constraint and load information. According to some embodiments, the deep learning model computer processor is further to receive design adjustments from the designer device. The received design adjustments might be for example, used to execute an optimization process and/or be fed back to continually re-train the deep learning model.

7.20200231466INTELLIGENT SYSTEMS AND METHODS FOR PROCESS AND ASSET HEALTH DIAGNOSIS, ANOMOLY DETECTION AND CONTROL IN WASTEWATER TREATMENT PLANTS OR DRINKING WATER PLANTS
US - 23.07.2020
Int.Class C02F 1/00
CCHEMISTRY; METALLURGY
02TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
FTREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
1Treatment of water, waste water, or sewage
 Appl.No 16472998 Applicant Zijun XIA Inventor Su LU

Described herein are systems and methods of analyzing data acquired from a water plant, both historical and in real-time, making determinations about process and asset health diagnosis and anomaly detection using advanced techniques, and controlling the plant and/or providing alerts based on such determinations.

8.20210287664Machine 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.

9.20180341632Conversation utterance labeling
US - 29.11.2018
Int.Class G06F 17/24
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
20Handling natural language data
21Text processing
24Editing, e.g. insert/delete
 Appl.No 15603091 Applicant International Business Machines Corporation Inventor Rama Kalyani T. Akkiraju

A method, a computer program product, and an information handling system is provided for labeling unlabeled utterances given a taxonomy of labels utilizing topic word semi-supervised learning.

10.WO/2019/071384INTELLIGENT SYSTEMS AND METHODS FOR PROCESS AND ASSET HEALTH DIAGNOSIS, ANOMOLY DETECTION AND CONTROL IN WASTEWATER TREATMENT PLANTS OR DRINKING WATER PLANTS
WO - 18.04.2019
Int.Class G01N 33/18
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
33Investigating or analysing materials by specific methods not covered by groups G01N1/-G01N31/131
18Water
 Appl.No PCT/CN2017/105377 Applicant BL TECHNOLOGIES, INC. Inventor LU, Su
Described herein are systems and methods of analyzing data acquired from a water plant, both historical and in real-time, making determinations about process and asset health diagnosis and anomaly detection using advanced techniques, and controlling the plant and/or providing alerts based on such determinations.
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