JP7815125B2 - プロセスサイクル画像の機械学習ベースの根本原因分析 - Google Patents

プロセスサイクル画像の機械学習ベースの根本原因分析

Info

Publication number
JP7815125B2
JP7815125B2 JP2022545420A JP2022545420A JP7815125B2 JP 7815125 B2 JP7815125 B2 JP 7815125B2 JP 2022545420 A JP2022545420 A JP 2022545420A JP 2022545420 A JP2022545420 A JP 2022545420A JP 7815125 B2 JP7815125 B2 JP 7815125B2
Authority
JP
Japan
Prior art keywords
image
failure
images
random forest
forest classifier
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
JP2022545420A
Other languages
English (en)
Japanese (ja)
Other versions
JPWO2021155291A5 (https=
JP2023512665A5 (https=
JP2023512665A (ja
Inventor
ナグメフ・レザエイ
ペドロ・ミギュエル・フェリペ・クルズ
Original Assignee
イルミナ インコーポレイテッド
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by イルミナ インコーポレイテッド filed Critical イルミナ インコーポレイテッド
Publication of JP2023512665A publication Critical patent/JP2023512665A/ja
Publication of JPWO2021155291A5 publication Critical patent/JPWO2021155291A5/ja
Publication of JP2023512665A5 publication Critical patent/JP2023512665A5/ja
Application granted granted Critical
Publication of JP7815125B2 publication Critical patent/JP7815125B2/ja
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G06V10/765—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects using rules for classification or partitioning the feature space
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G06F18/2148—Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the process organisation or structure, e.g. boosting cascade
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/2163—Partitioning the feature space
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
    • G06V10/507—Summing image-intensity values; Histogram projection analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/7715—Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
    • G06V10/7747—Organisation of the process, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/98—Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
    • G06V10/993—Evaluation of the quality of the acquired pattern
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/698—Matching; Classification
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6428—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes"
    • G01N2021/6439—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes" with indicators, stains, dyes, tags, labels, marks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6428—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10064—Fluorescence image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G06T2207/20028—Bilateral filtering
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20172—Image enhancement details
    • G06T2207/20192—Edge enhancement; Edge preservation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30072—Microarray; Biochip, DNA array; Well plate
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30168—Image quality inspection

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Multimedia (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Mathematical Physics (AREA)
  • Quality & Reliability (AREA)
  • Molecular Biology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Biotechnology (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Biomedical Technology (AREA)
  • Genetics & Genomics (AREA)
  • Immunology (AREA)
  • Chemical & Material Sciences (AREA)
  • Bioethics (AREA)
  • Epidemiology (AREA)
  • Public Health (AREA)
  • Optics & Photonics (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Pathology (AREA)
JP2022545420A 2020-01-31 2021-01-29 プロセスサイクル画像の機械学習ベースの根本原因分析 Active JP7815125B2 (ja)

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
US202062968950P 2020-01-31 2020-01-31
US62/968,950 2020-01-31
US17/161,595 US11715200B2 (en) 2020-01-31 2021-01-28 Machine learning-based root cause analysis of process cycle images
US17/161,595 2021-01-28
PCT/US2021/015906 WO2021155291A1 (en) 2020-01-31 2021-01-29 Machine learning-based root cause analysis of process cycle images

Publications (4)

Publication Number Publication Date
JP2023512665A JP2023512665A (ja) 2023-03-28
JPWO2021155291A5 JPWO2021155291A5 (https=) 2024-02-06
JP2023512665A5 JP2023512665A5 (https=) 2024-02-06
JP7815125B2 true JP7815125B2 (ja) 2026-02-17

Family

ID=77062917

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2022545420A Active JP7815125B2 (ja) 2020-01-31 2021-01-29 プロセスサイクル画像の機械学習ベースの根本原因分析

Country Status (7)

Country Link
US (1) US11715200B2 (https=)
EP (1) EP4097635B1 (https=)
JP (1) JP7815125B2 (https=)
KR (1) KR102767441B1 (https=)
CN (1) CN115004249B (https=)
AU (1) AU2021213252A1 (https=)
WO (1) WO2021155291A1 (https=)

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220284243A1 (en) * 2021-03-03 2022-09-08 International Business Machines Corporation Ensemble voting classifiers using adjusted thresholds
JP7532288B2 (ja) * 2021-03-04 2024-08-13 キオクシア株式会社 検査結果分析装置および検査結果分析プログラム
US12119070B2 (en) * 2021-05-06 2024-10-15 Micron Technology, Inc. Memory failure prediction
WO2024148157A1 (en) * 2023-01-06 2024-07-11 General Mills, Inc. Vision-based food product reformulation
US12303074B2 (en) 2023-03-31 2025-05-20 General Mills, Inc. Customized identification of ingredients in a storage system
CN116484263B (zh) * 2023-05-10 2024-01-05 江苏圣骏智能科技有限公司 一种智能化自助机故障检测系统及方法
CN116628598B (zh) * 2023-05-15 2024-03-12 生态环境部华南环境科学研究所(生态环境部生态环境应急研究所) 一种基于大数据和nmf模型的二噁英来源解析方法及系统
CN116861335A (zh) * 2023-05-31 2023-10-10 中铁二院工程集团有限责任公司 一种基于x射线荧光光谱的岩性智能判识方法及装置
CN117909886B (zh) * 2024-03-18 2024-05-24 南京海关工业产品检测中心 一种基于优化随机森林模型的锯齿棉品级分类方法及系统

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004361092A (ja) 2003-06-02 2004-12-24 Hitachi Software Eng Co Ltd Ｄｎａマイクロアレイイメージ解析システム
JP2008284166A (ja) 2007-05-17 2008-11-27 Nippon Hoso Kyokai <Nhk> 投球球種識別装置、識別器生成装置、投球球種識別プログラム及び識別器生成プログラム
WO2018140014A1 (en) 2017-01-25 2018-08-02 Athelas, Inc. Classifying biological samples using automated image analysis

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3054279A1 (en) 2015-02-06 2016-08-10 St. Anna Kinderkrebsforschung e.V. Methods for classification and visualization of cellular populations on a single cell level based on microscopy images
AU2016377391B2 (en) 2015-12-24 2022-09-01 Immunexpress Pty Ltd Triage biomarkers and uses therefor
JP2018005640A (ja) * 2016-07-04 2018-01-11 タカノ株式会社 分類器生成装置、画像検査装置、及び、プログラム
EP3482192A4 (en) * 2016-07-08 2020-08-05 ATS Automation Tooling Systems Inc. COMBINED AUTOMATIC AND MANUAL INSPECTION SYSTEM AND PROCESS
US11450121B2 (en) * 2017-06-27 2022-09-20 The Regents Of The University Of California Label-free digital brightfield analysis of nucleic acid amplification
JP6997964B2 (ja) * 2018-03-13 2022-02-04 オムロン株式会社 判別システム、判別装置、学習装置、判別方法及びプログラム
EP4404111A3 (en) * 2018-07-30 2024-10-23 Memorial Sloan Kettering Cancer Center Multi-modal, multi-resolution deep learning neural networks for segmentation, outcomes prediction and longitudinal response monitoring to immunotherapy and radiotherapy
US10931853B2 (en) 2018-10-18 2021-02-23 Sony Corporation Enhanced color reproduction for upscaling
CN109564677B (zh) * 2018-11-09 2022-09-27 香港应用科技研究院有限公司 基于随机森林分类器加权结果的超分辨率合成系统和方法
AU2020202249A1 (en) * 2020-03-30 2021-10-14 Anditi Pty Ltd Feature extraction from mobile lidar and imagery data
US11922623B2 (en) * 2020-07-13 2024-03-05 Aquyre Biosciences, Inc. Cellular diagnostic and analysis methods
US11860725B2 (en) * 2020-09-22 2024-01-02 Microsoft Technology Licensing, Llc. Failure recovery recommendations for CLI commands
US12333545B2 (en) * 2020-10-14 2025-06-17 Paypal, Inc. Probabilistic anomaly detection in streaming device data

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004361092A (ja) 2003-06-02 2004-12-24 Hitachi Software Eng Co Ltd Ｄｎａマイクロアレイイメージ解析システム
JP2008284166A (ja) 2007-05-17 2008-11-27 Nippon Hoso Kyokai <Nhk> 投球球種識別装置、識別器生成装置、投球球種識別プログラム及び識別器生成プログラム
WO2018140014A1 (en) 2017-01-25 2018-08-02 Athelas, Inc. Classifying biological samples using automated image analysis

Also Published As

Publication number Publication date
CN115004249A (zh) 2022-09-02
AU2021213252A1 (en) 2022-08-25
CA3166380A1 (en) 2021-08-05
WO2021155291A1 (en) 2021-08-05
CN115004249B (zh) 2025-10-28
EP4097635A1 (en) 2022-12-07
US20210241048A1 (en) 2021-08-05
US11715200B2 (en) 2023-08-01
KR20220134752A (ko) 2022-10-05
JP2023512665A (ja) 2023-03-28
EP4097635B1 (en) 2025-11-12
KR102767441B1 (ko) 2025-02-13

Similar Documents

Publication Publication Date Title
JP7815125B2 (ja) プロセスサイクル画像の機械学習ベースの根本原因分析
KR102924195B1 (ko) 프로세스 사이클 이미지의 딥 러닝 기반 근본 원인 분석
JP7760165B2 (ja) 人工知能（ａｉ）モデルを使用した非侵襲的遺伝子検査を行う方法及びシステム
US7958063B2 (en) Methods and systems for identifying and localizing objects based on features of the objects that are mapped to a vector
Darapureddy et al. Research of machine learning algorithms using k-fold cross validation
US20240411296A1 (en) Computer implemented method for the detection and classification of anomalies in an imaging dataset of a wafer, and systems making use of such methods
US20230044794A1 (en) Neural network training device, system and method
US20230186109A1 (en) Machine learning-based genotyping process outcome prediction using aggregate metrics
Vigdor et al. Accurate and fast off and online fuzzy ARTMAP-based image classification with application to genetic abnormality diagnosis
Ghadhban et al. Segments interpolation extractor for finding the best fit line in Arabic offline handwriting recognition words
CA3166380C (en) Machine learning-based root cause analysis of process cycle images
Chowdhury et al. Genetic algorithm based global and local feature selection approach for handwritten numeral recognition
Castleman et al. Object Classification
Sankari et al. XGBoost-Powered Hybrid Ensemble Approach for Precise Brain Tumor Diagnosis, Combining Convolutional Neural Networks, ResNet50 and K-Means Clustering Techniques
Dhar et al. A hybrid system for detecting semiconductor wafer defects using modified MobileNet with multi-head attention
Husnain et al. English letter classification using bayesian decision theory and feature extraction using principal component analysis
BEN KAMOUCHE Binarization of Degraded Document Images Using Deep Learning Techniques
García-Ramírez et al. Facial Expression Recognition Using Interpolation

Legal Events

Date Code Title Description
A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20220802

A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20240124

A621 Written request for application examination

Free format text: JAPANESE INTERMEDIATE CODE: A621

Effective date: 20240124

A871 Explanation of circumstances concerning accelerated examination

Free format text: JAPANESE INTERMEDIATE CODE: A871

Effective date: 20240124

A977 Report on retrieval

Free format text: JAPANESE INTERMEDIATE CODE: A971007

Effective date: 20240408

A131 Notification of reasons for refusal

Free format text: JAPANESE INTERMEDIATE CODE: A131

Effective date: 20240422

A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20240717

A02 Decision of refusal

Free format text: JAPANESE INTERMEDIATE CODE: A02

Effective date: 20240813

A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20241211

A911 Transfer to examiner for re-examination before appeal (zenchi)

Free format text: JAPANESE INTERMEDIATE CODE: A911

Effective date: 20241219

A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20250702

A61 First payment of annual fees (during grant procedure)

Free format text: JAPANESE INTERMEDIATE CODE: A61

Effective date: 20260204

R150 Certificate of patent or registration of utility model

Ref document number: 7815125

Country of ref document: JP

Free format text: JAPANESE INTERMEDIATE CODE: R150