IP Library › Granted Patent US 12,731,259
Granted Patent B2
US 12,731,259 · App. 17/584,233 · Granted Sep 8, 2026

Generating object mask previews and single input selection object masks

Inventors: Betty Leong (Los Altos, CA); Hyunghwan Byun (Mountain View, CA); Alan L Erickson (Highlands Ranch, CO); Chih-Yao Hsieh (San Jose, CA); Sarah Kong (Cupertino, CA); Seyed Morteza Safdarnejad (San Jose, CA); Salil Tambe (San Jose, CA); Yilin Wang (San Jose, CA); Zijun Wei (San Jose, CA); Zhengyun Zhang (San Jose, CA)
Assignee: Adobe Inc.
G06T7/10G06F3/04842G06T3/4046G06T2207/20084G06T2207/20092
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Quick Facts
Patent No.
US 12,731,259
App. No.
17/584,233
Granted
Sep 8, 2026
Kind
B2
Abstract

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate preliminary object masks for objects in an image, surface the preliminary object masks as object mask previews, and on-demand converts preliminary object masks into refined object masks. Indeed, in one or more implementations, an object mask preview and on-demand generation system automatically detects objects in an image. For the detected objects, the object mask preview and on-demand generation system generates preliminary object masks for the detected objects of a first lower resolution. The object mask preview and on-demand generation system surfaces a given preliminary object mask in response to detecting a first input. The object mask preview and on-demand generation system also generates a refined object mask of a second higher resolution in response to detecting a second input.

Claims (59)

1 . A computer-implemented method comprising:

generating, jointly utilizing a first neural network and in response to receiving a digital image, preliminary object masks for a plurality of objects in the digital image, wherein each preliminary object mask corresponds to an object of the plurality of objects in the digital image;

receiving, via a graphical user interface, a first user input indicating a first object of the plurality of objects in the digital image;

displaying, in response to the first user input, a first preliminary object mask for the first object via the graphical user interface, the first preliminary object mask having a first resolution;

receiving, via the graphical user interface, a second user input indicating a second object of the plurality of objects in the digital image;

displaying, in response to the second user input, a second preliminary object mask for the second object via the graphical user interface, the second preliminary object mask having the first resolution, while also removing display of the first preliminary object mask;

detecting, via the graphical user interface, a third user input selecting the displayed second preliminary object mask for the second object via the graphical user interface; and

in response to the third user input selecting the second preliminary object mask for the second object, generating a refined object mask for the second object utilizing a second neural network that requires longer processing time than the first neural network, the refined object mask having a second resolution that is greater than the first resolution.

2 . The computer-implemented method of claim 1 , further comprising detecting the first user input by detecting a hovering pointer over the first object or a touch tap gesture on the first object.

3 . The computer-implemented method of claim 1 , wherein generating the preliminary object masks for the plurality of objects comprises generating preliminary object masks having a lower resolution than the digital image.

4 . The computer-implemented method of claim 1 , further comprising:

detecting an additional user input; and

displaying the preliminary object masks for the plurality of objects simultaneously in response to the additional user input.

5 . The computer-implemented method of claim 1 , wherein generating the preliminary object masks for the plurality of objects further comprises generating the preliminary object masks for the plurality of objects in response to selection of an option to mask all objects in the digital image without further user input.

6 . The computer-implemented method of claim 1 , further comprising:

while the first preliminary object mask for the first object is displayed, receiving user input to select the second object;

merging the first preliminary object mask for the first object and the second preliminary object mask for the second object into a merged preliminary object mask; and

displaying the merged preliminary object mask via the graphical user interface.

7 . The computer-implemented method of claim 1 , further comprising:

receiving a user selection of a third preliminary object mask for a third object;

in response to the user selection of the third preliminary object mask for the third object, generating a refined object mask for the third object, wherein the refined object mask for the second object has a higher resolution than the third preliminary object mask for the third object; and

displaying the refined object mask for the third object via the graphical user interface.

8 . A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause a computing device to perform operations comprising:

generating, jointly utilizing a first neural network and in response to receiving a digital image, preliminary object masks for one or more objects in the digital image, wherein each preliminary object mask corresponds to an object of the one or more objects in the digital image;

displaying the digital image via a graphical user interface;

detecting, via the graphical user interface, a first user input indicating first object of the one or more objects;

in response to the first user input, displaying via the graphical user interface a first preliminary object mask for the first object, the first preliminary object mask having a first resolution;

receiving, via the graphical user interface, a second user input indicating a second object of the one or more objects in the digital image;

displaying, in response to the second user input, a second preliminary object mask for the second object via the graphical user interface, the second preliminary object mask having the first resolution, while also removing display of the first preliminary object mask;

detecting, via the graphical user interface, a third user input selecting the displayed second preliminary object mask for the second object via the graphical user interface; and

in response to the third user input selecting the second preliminary object mask for the second object, generating a refined object mask for the second object utilizing a second neural network that requires longer processing time than the first neural network, the refined object mask having a second resolution that is greater than the first resolution.

9 . The non-transitory computer readable medium of claim 8 ,

wherein detecting, via the graphical user interface, the first user input indicating the first object comprises detecting that a cursor is hovering over the object.

10 . The non-transitory computer readable medium of claim 9 , wherein detecting, via the graphical user interface, the third user input selecting the second preliminary object mask for the second object comprises detecting a click or tap on the second preliminary object mask.

11 . The non-transitory computer readable medium of claim 8 , wherein generating the refined object mask for the second object comprises refining and upscaling the second preliminary object mask utilizing a segmentation refinement neural network remote from the computing device.

12 . The non-transitory computer readable medium of claim 11 , wherein generating the refined object mask for the second object comprises:

generating a revised preliminary object mask utilizing an object selection model; and

refining and upscaling the revised preliminary object mask utilizing the segmentation refinement neural network.

13 . The non-transitory computer readable medium of claim 11 , wherein generating preliminary object masks for the one or more objects comprises generating the preliminary object masks utilizing a panoptic segmentation neural network on the computing device.

14 . The non-transitory computer readable medium of claim 13 , wherein generating the preliminary object masks utilizing the panoptic segmentation neural network comprises:

detecting objects in the digital image utilizing one or more detection heads of the panoptic segmentation neural network; and

for each object detected in the digital image, generating, utilizing a masking head of the panoptic segmentation neural network, a preliminary object mask.

15 . The non-transitory computer readable medium of claim 8 , wherein generating preliminary object masks for the one or more objects comprises:

generating initial object masks for the one or more objects; and

refining the initial object masks to generate the preliminary object masks utilizing a segmentation refinement neural network.

16 . The non-transitory computer readable medium of claim 8 , further comprising instructions that when executed by the at least one processor cause the computing device to perform further operations comprising:

receiving a selection to generate refined masks for all objects in the digital image; and

generating refined object masks for the one or more objects from the preliminary object masks for the one or more objects.

17 . A system comprising:

one or more memory devices storing a panoptic segmentation neural network and a segmentation refinement neural network; and

at least one processor configured to cause the system to:

generate, in response to receiving a digital image, preliminary object masks for objects in the digital image utilizing the panoptic segmentation neural network, wherein each preliminary object mask corresponds to an object of the objects in the digital image;

display the digital image via a graphical user interface;

in response to a first user input indicating first object of the objects in the digital image, display a first preliminary object mask for the first object via the graphical user interface, the first preliminary object mask having a first resolution;

in response to a second user input indicating a second object of the objects in the digital image, display a second preliminary object mask for the second object via the graphical user interface, the second preliminary object mask having the first resolution, while also removing display of the first preliminary object mask; and

in response to a third user input selecting the displayed second preliminary object mask for the second object, generate a refined object mask for the second object utilizing the segmentation refinement neural network that requires longer processing time than the panoptic segmentation neural network, the refined object mask having a second resolution that is greater than the first resolution.

18 . The system as recited in claim 17 , wherein the at least one processor is configured to cause the system to generate the preliminary object masks by generating object masks having the first resolution.

19 . The system as recited in claim 18 , wherein the at least one processor is configured to cause the system to generate the refined object mask for the second object by refining and upscaling the second preliminary object mask for the second object to the second resolution.

20 . The system as recited in claim 17 , wherein the at least one processor is configured to cause the system to generate the preliminary object masks for the objects in the digital image in response to a single user input.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 25, 2022
From: LEONG, BETTY; BYUN, HYUNGHWAN; ERICKSON, ALAN L; HSIEH, CHIH-YAO; KONG, SARAH; SAFDARNEJAD, SEYED MORTEZA; TAMBE, SALIL; WANG, YILIN; WEI, ZIJUN; ZHANG, ZHENGYUN
To: ADOBE INC.
Reel/Frame 058765/0735 →
Continuity (2)
Provisional Application 63271147 · Oct 23, 2021
Related Publication 20230129341A1 · Apr 27, 2023
References Cited (299)
US 5214744A · Schweizer et al. · 1993 [cited by applicant]
US 6993174B2 · Fan et al. · 2006 [cited by applicant]
US 7031519B2 · Elmenhurst · 2006 [cited by applicant]
US 7295700B2 · Schiller et al. · 2007 [cited by applicant]
US 7606417B2 · Steinberg et al. · 2009 [cited by applicant]
US 7916917B2 · Dewaele et al. · 2011 [cited by applicant]
US 8600143B1 · Kulkarni et al. · 2013 [cited by applicant]
US 8675934B2 · Wehnes et al. · 2014 [cited by applicant]
US 9251429B2 · Pham et al. · 2016 [cited by applicant]
US 9336483B1 · Abeysooriya et al. · 2016 [cited by applicant]
US 9342869B2 · Wang et al. · 2016 [cited by applicant]
US 9418319B2 · Shen et al. · 2016 [cited by applicant]
US 9443316B1 · Takeda · 2016 [cited by examiner]
US 9495756B2 · Rivet-Sabourin · 2016 [cited by applicant]
US 9684967B2 · Abedini et al. · 2017 [cited by applicant]
US 10109051B1 · Natesh et al. · 2018 [cited by applicant]
US 10192129B2 · Price et al. · 2019 [cited by applicant]
US 10210613B2 · Xu et al. · 2019 [cited by applicant]
US 10460214B2 · Lu et al. · 2019 [cited by applicant]
US 10470510B1 · Koh et al. · 2019 [cited by applicant]
US 10643331B2 · Ghesu et al. · 2020 [cited by applicant]
US 10679046B1 · Black et al. · 2020 [cited by applicant]
US 10846566B2 · Zhu et al. · 2020 [cited by applicant]
US 11282208B2 · Cohen et al. · 2022 [cited by applicant]
US 11335004B2 · Liu et al. · 2022 [cited by applicant]
US 11430129B2 · Barish · 2022 [cited by applicant]
US 11568627B2 · Price et al. · 2023 [cited by applicant]
US 11676279B2 · Price et al. · 2023 [cited by applicant]
US 11875254B2 · Rhodes et al. · 2024 [cited by applicant]
US 20010051852A1 · Sundaravel et al. · 2001 [cited by applicant]
US 20030081833A1 · Tilton · 2003 [cited by applicant]
US 20040042662A1 · Wilensky et al. · 2004 [cited by applicant]
US 20040126013A1 · Olson · 2004 [cited by applicant]
US 20040190092A1 · Silverbrook et al. · 2004 [cited by applicant]
US 20040202368A1 · Lee et al. · 2004 [cited by applicant]
US 20050264557A1 · Kise · 2005 [cited by applicant]
US 20060013455A1 · Watson et al. · 2006 [cited by applicant]
US 20060015373A1 · Cuypers · 2006 [cited by applicant]
US 20060045336A1 · Lim · 2006 [cited by applicant]
US 20060285743A1 · Oh et al. · 2006 [cited by applicant]
US 20070165949A1 · Sinop et al. · 2007 [cited by applicant]
US 20090003699A1 · Dugan et al. · 2009 [cited by applicant]
US 20090252429A1 · Prochazka et al. · 2009 [cited by applicant]
US 20100183225A1 · Vantaram et al. · 2010 [cited by applicant]
US 20100226566A1 · Luo et al. · 2010 [cited by applicant]
US 20100322488A1 · Virtue et al. · 2010 [cited by applicant]
US 20110188720A1 · Narayanan et al. · 2011 [cited by applicant]
US 20110216975A1 · Rother et al. · 2011 [cited by applicant]
US 20110285874A1 · Showering et al. · 2011 [cited by applicant]
US 20120201423A1 · Onai et al. · 2012 [cited by applicant]
US 20130182909A1 · Rodriguez-Serrano · 2013 [cited by applicant]
US 20130223740A1 · Wang et al. · 2013 [cited by applicant]
US 20130259374A1 · He et al. · 2013 [cited by applicant]
US 20140007022A1 · Tocino Diaz · 2014 [cited by examiner]
US 20140010449A1 · Haaramo et al. · 2014 [cited by applicant]
US 20140056472A1 · Gu · 2014 [cited by applicant]
US 20140056520A1 · Rodriguez Serrano · 2014 [cited by applicant]
US 20140247978A1 · Devin et al. · 2014 [cited by applicant]
US 20140334667A1 · Eswara et al. · 2014 [cited by applicant]
US 20150117783A1 · Lin et al. · 2015 [cited by applicant]
US 20150170002A1 · Szegedy et al. · 2015 [cited by applicant]
US 20150269427A1 · Kim et al. · 2015 [cited by applicant]
US 20160189010A1 · Tang et al. · 2016 [cited by applicant]
US 20160232425A1 · Huang et al. · 2016 [cited by applicant]
US 20160358035A1 · Ruan et al. · 2016 [cited by applicant]
US 20170032551A1 · Fried et al. · 2017 [cited by applicant]
US 20170039723A1 · Price et al. · 2017 [cited by applicant]
US 20170068416A1 · Li · 2017 [cited by examiner]
US 20170076443A1 · Ye et al. · 2017 [cited by applicant]
US 20170103258A1 · Yu et al. · 2017 [cited by applicant]
US 20170109625A1 · Dai et al. · 2017 [cited by applicant]
US 20170116497A1 · Georgescu et al. · 2017 [cited by applicant]
US 20170140236A1 · Price et al. · 2017 [cited by applicant]
US 20170169313A1 · Choi et al. · 2017 [cited by applicant]
US 20170169567A1 · Chefd'hotel et al. · 2017 [cited by applicant]
US 20170213112A1 · Sachs et al. · 2017 [cited by applicant]
US 20170213349A1 · Kuo et al. · 2017 [cited by applicant]
US 20170231550A1 · Do et al. · 2017 [cited by applicant]
US 20170244908A1 · Flack et al. · 2017 [cited by applicant]
US 20170249739A1 · Kallenberg et al. · 2017 [cited by applicant]
US 20170287137A1 · Lin et al. · 2017 [cited by applicant]
US 20180061046A1 · Bozorgtabar et al. · 2018 [cited by applicant]
US 20180108137A1 · Price et al. · 2018 [cited by applicant]
US 20180137335A1 · Kim et al. · 2018 [cited by applicant]
US 20180182101A1 · Petersen et al. · 2018 [cited by applicant]
US 20180240243A1 · Kim et al. · 2018 [cited by applicant]
US 20180293707A1 · El-Khamy · 2018 [cited by examiner]
US 20180307946A1 · Kuroda et al. · 2018 [cited by applicant]
US 20180365813A1 · Leong · 2018 [cited by examiner]
US 20190057507A1 · El-Khamy et al. · 2019 [cited by applicant]
US 20190080456A1 · Song et al. · 2019 [cited by applicant]
US 20190108414A1 · Price et al. · 2019 [cited by applicant]
US 20190130229A1 · Lu et al. · 2019 [cited by applicant]
US 20190205606A1 · Zhou et al. · 2019 [cited by applicant]
US 20190236394A1 · Price et al. · 2019 [cited by applicant]
US 20190236786A1 · McNerney · 2019 [cited by examiner]
US 20190340462A1 · Pao et al. · 2019 [cited by applicant]
US 20190357615A1 · Koh et al. · 2019 [cited by applicant]
US 20190377487A1 · Bailey · 2019 [cited by examiner]
US 20200020108A1 · Pao · 2020 [cited by examiner]
US 20200143194A1 · Hou et al. · 2020 [cited by applicant]
US 20200167930A1 · Wang et al. · 2020 [cited by applicant]
US 20200218961A1 · Kanazawa · 2020 [cited by examiner]
US 20200302173A1 · Deng · 2020 [cited by examiner]
US 20200320273A1 · Li · 2020 [cited by applicant]
US 20200388071A1 · Grabner et al. · 2020 [cited by applicant]
US 20210027098A1 · Ge et al. · 2021 [cited by applicant]
US 20210082118A1 · Zhang et al. · 2021 [cited by applicant]
US 20210158043A1 · Hou · 2021 [cited by examiner]
US 20210217178A1 · Terzopoulos et al. · 2021 [cited by applicant]
US 20210248748A1 · Turgutlu et al. · 2021 [cited by applicant]
US 20210290096A1 · Yang · 2021 [cited by applicant]
US 20210295507A1 · Nie · 2021 [cited by applicant]
US 20220044366A1 · Zhang et al. · 2022 [cited by applicant]
US 20220044407A1 · Liu et al. · 2022 [cited by applicant]
US 20220108454A1 · Tsai et al. · 2022 [cited by applicant]
US 20220237799A1 · Price et al. · 2022 [cited by applicant]
US 20220262009A1 · Yu et al. · 2022 [cited by applicant]
US 20220292684A1 · Wang et al. · 2022 [cited by applicant]
US 20220375079A1 · Finley et al. · 2022 [cited by applicant]
US 20230281763A1 · Zhang et al. · 2023 [cited by applicant]
CN 106022273A · 2016 [cited by applicant]
CN 106339591A · 2017 [cited by applicant]
CN 107103315A · 2017 [cited by applicant]
CN 108345890A · 2018 [cited by applicant]
CN 110232689A · 2019 [cited by applicant]
DE 102015207047A1 · 2015 [cited by applicant]
WO 2015177268A1 · 2015 [cited by applicant]
WO 2018229490A1 · 2018 [cited by applicant]
He et al. in Guided Image Filtering, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, 2013. [cited by applicant]
He et al. in Fast Guided Filter, Computer Vision and Pattern Recognition, arXiv:1505.00996, 2015. [cited by applicant]
Ning Xu et al., “Deep GrabCut for Object Selection,” published Jul. 14, 2017. [cited by applicant]
Zhao et al., in Pyramid scene parsing network, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2881-2890, 2017. [cited by applicant]
He et al. in Deep Residual Learning For Image Recognition In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016. [cited by applicant]
Howard et al., in Searching For Mobilenetv3, In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324, 2019. [cited by applicant]
A. Criminisi, T. Sharp, and A. Blake. GeoS: Geodesic image segmentation. In ECCV, pp. 99-112, 2008. [cited by applicant]
A. Guzman-rivera, D. Batra, and P. Kohli. Multiple choice learning: Learning to produce multiple structured outputs. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, NIPS, pp. 1799-1807. 2012. [cited by applicant]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pp. 1097-1105, 2012. [cited by applicant]
Alexander Kirillov, Yuxin Wu, Kaiming He, and Ross Girshick. Pointrend: Image segmentation as rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9799-9808, 2020. [cited by applicant]
Ali Borji and Laurent Itti. State-of-the-art in visual attention modeling. IEEE transactions on pattern analysis and machine intelligence, 35(1):185-207, 2012. [cited by applicant]
Ali Borji, Ming-Ming Cheng, Qibin Hou, Huaizu Jiang, and Jia Li. Salient object detection: A survey. Computational visual media, pp. 1-34, 2019. [cited by applicant]
B. Hariharan, P. Arbelaez, L. Bourdev, S. Maji, and J. Malik. Semantic contours from inverse detectors. 2011. [cited by applicant]
B. L. Price, B. Morse, and S. Cohen. Geodesic graph cut for interactive image segmentation. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 3161-3168. IEEE, 2010. [cited by applicant]
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba. Scene parsing through ade20k dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. [cited by applicant]
C. Rother, V. Kolmogorov, and A. Blake. Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics (TOG), 23(3):309-314, 2004. [cited by applicant]
Chao Peng, Xiangyu Zhang, Gang Yu, Guiming Luo, and Jian Sun. Large kernel matters-improve semantic segmentation by global convolutional network. In Proceedings of the IEEE conference on computer vision and pattern reco… [cited by applicant]
Chen et al, ‘DISC: Deep Image Saliency Computing via Progressive Representation Learning’, 2016, IEEE Transactions on Neural Networks and Learning Systems, vol. 27, No. 6, pp. 1135-1149 (Year: 2016). [cited by applicant]
Chen, Liang-Chieh et al. “Rethinking Atrous Convolution for Semantic Image Segmentation.” ArXiv abs/1706.05587 (2017): n. pag. [cited by applicant]
Cheng, Ho & Chung, Jihoon & Tai, Yu-Wing & Tang, Chi-Keung. (2020). CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement. arXiv:2005.02551v1 [cs.CV] May 6, 2020. [cited by applicant]
Chi Zhang, Guosheng Lin, Fayao Liu, Rui Yao, and Chunhua Shen. Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. In Proceedings of the IEEE Conference on Computer Vis… [cited by applicant]
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference on… [cited by applicant]
Chuan Yang, Lihe Zhang, Huchuan Lu, Xiang Ruan, and Ming-Hsuan Yang. Saliency detection via graph-based manifold ranking. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3166-3173, … [cited by applicant]
Combined Search & Examination Report as received in UK application GB1813276.1 dated Feb. 14, 2019. [cited by applicant]
Combined Search and Examination Report as received in UK application GB1915436.8 dated Aug. 12, 2020. [cited by applicant]
D. Acuna, H. Ling, A. Kar, and S. Fidler. Efficient interactive annotation of segmentation datasets with Polygon-RNN++. In CVPR, 2018. [cited by applicant]
D. Batra, P. Yadollahpour, A. Guzman-Rivera, and G. Shakhnarovich. Diverse m-best solutions in markov random fields. In ECCV, 2012. [cited by applicant]
D. Freedman and T. Zhang. Interactive graph cut based segmentation with shape priors. In IEEE CVPR, vol. 1, pp. 755-762. IEEE, 2005. [cited by applicant]
Dominik A Klein and Simone Frintrop. Center-surround divergence of feature statistics for salient object detection. In 2011 International Conference on Computer Vision, pp. 2214-2219. IEEE, 2011. [cited by applicant]
E. N. Mortensen and W. A. Barrett. Intelligent scissors for image composition. In Proceedings of the 22nd annual conference on Computer graphics and interactive techniques, pp. 191-198, 1995. [cited by applicant]
Examination Report as received in Australian application 2019250107 dated Nov. 5, 2021. [cited by applicant]
Examination Report as received in Australian application 2019250107 dated Oct. 14, 2021. [cited by applicant]
Farag, A.—“A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling”—May 22, 2015—Elsevier Journal of Medical Image Analysis, pp. 1-21. [cited by applicant]
G. Lin, C. Shen, I. Reid, et al. Efficient piecewise training of deep structured models for semantic segmentation. arXiv preprint arXiv:1504.01013, 2015. [cited by applicant]
Gao Huang, Zhuang Liu, Kilian Q. Weinberger, and Laurens V.D. Maaten; “Densely connected convolutional networks,” In arXiv:1608.06993v3, 2016. [cited by applicant]
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708, 2017. [cited by applicant]
Guanbin Li and Yizhou Yu. Visual saliency based on multi-scale deep features. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5455-5463, 2015. [cited by applicant]
Guo, Z.—“Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes”—arXiv—Nov. 2, 2017—pp. 1-10 (Year: 2017). [cited by applicant]
Guo, Z.—“Deep Learning-Based Image Segmentation on Multimodal Medical Imaging”—IEEE—Mar. 1, 2019—pp. 162-169 ( Year: 2019). [cited by applicant]
Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid. Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognitio… [cited by applicant]
Guosheng Lin, Chunhua Shen, Anton Van Den Hengel, and Ian Reid. Efficient piecewise training of deep structured models for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern reco… [cited by applicant]
H. Le, L. Mai, B. Price, S. Cohen, H. Jin, and F. Liu. Interactive boundary prediction for object selection. In ECCV, Sep. 2018. [cited by applicant]
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2881-2890, 2017. [cited by applicant]
Ho Kei Cheng, Jihoon Chung, Yu-Wing Tai, and Chi-Keung Tang. Cascadepsp: Toward class-agnostic and very high-resolution segmentation via global and local refinement. In Proceedings of the IEEE/CVF Conference on Computer… [cited by applicant]
IEEE Conference On Computer Vision and Pattern Recognition (CVPR), 2016, Liang Chieh Chen et al., “Attention to Scale: Scale-Aware Semantic Image Segmentation”, pp. 3640-3649 abstract 1. 7-9 and p. 2 left column 1st par… [cited by applicant]
IEEE/CVF International Conference On Computer Vision (ICCV), 2019, Liew Jun Hao et al, “MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input”, pp. 662-670 the whole document. [cited by applicant]
Intention to Grant as received in UK application GB1915436.8 dated Aug. 25, 2021. [cited by applicant]
J. H. Liew, Y. Wei, W. Xiong, S.-H. Ong, and J. Feng. Regional interactive image segmentation networks. In IEEE ICCV, Oct. 2017. [cited by applicant]
J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. arXiv preprint arXiv:1411.4038, 2014. [cited by applicant]
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788, 2016. [cited by applicant]
Jia-Xing Zhao, Jiang-Jiang Liu, Deng-Ping Fan, Yang Cao, Jufeng Yang, and Ming-Ming Cheng. Egnet: Edge guidance network for salient object detection. In Proceedings of the IEEE International Conference on Computer Visio… [cited by applicant]
Jianming Zhang and Stan Sclaroff. Saliency detection: A boolean map approach. In Proceedings of the IEEE international conference on computer vision, pp. 153-160, 2013. [cited by applicant]
Jianping Shi, Qiong Yan, Li Xu, and Jiaya Jia. Hierarchical image saliency detection on extended cssd. IEEE transactions on pattern analysis and machine intelligence, 38(4):717-729, 2015. [cited by applicant]
Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al. Deep high-resolution representation learning for visual recognition. IEEE transactions… [cited by applicant]
Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431-3440, 2015. [cited by applicant]
K. Maninis, S. Caelles, J. Pont-Tuset, and L. Van Gool. Deep extreme cut: From extreme points to object segmentation. In IEEE CVPR, 2018. [cited by applicant]
K. McGuinness and N. E. OConnor. Toward automated evaluation of interactive segmentation. Computer Vision and Image Understanding, 115(6):868-884, 2011. [cited by applicant]
K. Yamaguchi, M. H. Kiapour, L. E. Ortiz, and T. L. Berg. Parsing clothing in fashion photographs. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 3570-3577. IEEE, 2012. [cited by applicant]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016. [cited by applicant]
Kamat, S. P.; Control Theory and Informatics, vol. 2, No. 1, 2012 Digital Image Processing for Camera Application in Mobile Devices using Artificial Neural Networks, pp. 11-17. [cited by applicant]
Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. [cited by applicant]
L. Castrejon, K. Kundu, R. Urtasun, and S. Fidler. Annotating object instances with a polygon-rnn. In IEEE CVPR, Jul. 2017. [cited by applicant]
L. Grady. Random walks for image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(11):1768-1783, 2006. Part 1. [cited by applicant]
L. Grady. Random walks for image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(11):1768-1783, 2006. Part 2. [cited by applicant]
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062, 2014. [cited by applicant]
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv preprint arXiv:1802.02611, 2018. [cited by applicant]
Laurent Itti, Christof Koch, and Ernst Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on pattern analysis and machine intelligence, 20(11):1254-1259, 1998. [cited by applicant]
Le, T., & Duan, Y. (2020). REDN: a recursive encoder-decoder network for edge detection. IEEE Access, 8, 90153-90164. (Year: 2020). [cited by applicant]
Li et al., Interactive Image Segmentation with Latent Diversity, 2018, IEEE 2575-7075/18, DOI 10.11/09/CVPR. 2018.00067, pp. 577-585. (Year: 2018). [cited by applicant]
Lihe Zhang, Chuan Yang, Huchuan Lu, Xiang Ruan, and Ming-Hsuan Yang. Ranking saliency. IEEE transactions on pattern analysis and machine intelligence, 39(9):1892-1904, 2016. [cited by applicant]
Lihe Zhang, Jianwu Ai, Bowen Jiang, Huchuan Lu, and Xiukui Li. Saliency detection via absorbing markov chain with learnt transition probability. IEEE Transactions on Image Processing, 27(2):987-998, 2017. [cited by applicant]
Lijun Wang, Huchuan Lu, Xiang Ruan, and Ming-Hsuan Yang. Deep networks for saliency detection via local estimation and global search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.… [cited by applicant]
Lijun Wang, Huchuan Lu, Yifan Wang, Mengyang Feng, Dong Wang, Baocai Yin, and Xiang Ruan. Learning to detect salient objects with image-level supervision. In Proceedings of the IEEE Conference on Computer Vision and Pat… [cited by applicant]
Linzhao Wang, Lijun Wang, Huchuan Lu, Pingping Zhang, and Xiang Ruan. Saliency detection with recurrent fully convolutional networks. In European conference on computer vision, pp. 825-841. Springer, 2016. [cited by applicant]
Lu Zhang, Ju Dai, Huchuan Lu, You He, and Gang Wang. A bi-directional message passing model for salient object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1741-1750, … [cited by applicant]
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (VOC) challenge. IJCV, 88(2):303-338, 2010. [cited by applicant]
M. Firman, N. D. F. Campbell, L. Agapito, and G. J. Brostow. Diversenet: When one right answer is not enough. In IEEE CVPR, Jun. 2018. [cited by applicant]
M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. IJCV, 1(4):321-331, 1988. [cited by applicant]
M. Rajchl et al., “DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks,” in IEEE Transactions on Medical Imaging, vol. 36, No. 2, pp. 674-683, Feb. 2017, archived at arxiv.org/… [cited by applicant]
Ming-Ming Cheng, Niloy J Mitra, Xiaolei Huang, Philip H S Torr, and Shi-Min Hu. Global contrast based salient region detection. IEEE transactions on pattern analysis and machine intelligence, 37(3):569-582, 2014. [cited by applicant]
N. Xu, B. Price, S. Cohen, J. Yang, and T. S. Huang. Deep interactive object selection. In IEEE CVPR, pp. 373-381, Mar. 13, 2016. [cited by applicant]
Nian Liu and Junwei Han. Dhsnet: Deep hierarchical saliency network for salient object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 678-686, 2016. [cited by applicant]
Nian Liu, Junwei Han, and Ming-Hsuan Yang. Picanet: Learning pixel-wise contextual attention for saliency detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3089-3098, 2018. [cited by applicant]
Ning Xu, Brian Price, Scott Cohen, and Thomas Huang. Deep image matting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2970-2979, 2017. [cited by applicant]
Notice of Grant as received in Australian application 2019250107 dated Mar. 17, 2022. [cited by applicant]
Notice of Grant as received in UK application GB1813276.1 dated Oct. 12, 2021. [cited by applicant]
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. Imagenet large scale visual recognition challenge. IJCV, 115(3):211-252, 2015. [cited by applicant]
Office Action as received in CN application 201810886944.1 dated Apr. 8, 2023. [cited by applicant]
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pp. 234-241… [cited by applicant]
Philipp Krahenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In Advances in neural information processing systems, pp. 109-117, 2011. [cited by applicant]
Pingping Zhang, Dong Wang, Huchuan Lu, Hongyu Wang, and Xiang Ruan. Amulet: Aggregating multi-level convolutional features for salient object detection. In Proceedings of the IEEE International Conference on Computer Vi… [cited by applicant]
R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pp. 580-58… [cited by applicant]
Robert Osserman et al. The isoperimetric inequality. Bulletin of the American Mathematical Society, 84(6):1182-1238, 1978. [cited by applicant]
Roth, H.—“DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation”—Jun. 22, 2015—arXiv:1506.06448v1, pp. 1-12. [cited by applicant]
Rui Zhao, Wanli Ouyang, Hongsheng Li, and Xiaogang Wang. Saliency detection by multi-context deep learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1265-1274, 2015. [cited by applicant]
S. Lee, S. Purushwalkam Shiva Prakash, M. Cogswell, D. Crandall, and D. Batra. Why M heads are better than one: Training a diverse ensemble of deep networks. CoRR, abs/1511.06314, 2015. [cited by applicant]
S. Lee, S. Purushwalkam Shiva Prakash, M. Cogswell, V. Ranjan, D. Crandall, and D. Batra. Stochastic multiple choice learning for training diverse deep ensembles. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and … [cited by applicant]
S. Mahadevan, P. Voigtlaender, and B. Leibe. Iteratively trained interactive segmentation. arXiv preprint arXiv:1805.04398, 2018. [cited by applicant]
S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pp. 91-99, 2015. [cited by applicant]
S. Vicente, V. Kolmogorov, and C. Rother. Graph cut based image segmentation with connectivity priors. In IEEE CVPR, pp. 1-8. IEEE, 2008. [cited by applicant]
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. Torr. Conditional random fields as recurrent neural networks. arXiv preprint arXiv:1502.03240, 2015. [cited by applicant]
Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, and Philip HS Torr. Conditional random fields as recurrent neural networks. In Proceedings of the IEEE inter… [cited by applicant]
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pp. 740-7… [cited by applicant]
Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, Piotr Dollár; “Microsoft COCO: Common Objects in Context,” Submitted on May 1, 2… [cited by applicant]
V. Gulshan, C. Rother, A. Criminisi, A. Blake, and A. Zisserman. Geodesic star convexity for interactive image segmentation. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 3129-3136. IEE… [cited by applicant]
Wang, G.—“Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning”—arXiv—Oct. 11, 2017—pp. 1-11 (Year: 2017). [cited by applicant]
Wang, N.—“Transferring Rich Feature Hierarchies for Robust Visual Tracking”—Apr. 23, 2015—arXiv:1501.04587v2, pp. 1-9. [cited by applicant]
Wang, Y., Zhao, X., Li, Y., & Huang, K. (2018). Deep crisp boundaries: From boundaries to higher-level tasks. IEEE Transactions on Image Processing, 28(3), 1285-1298. (Year: 2018). [cited by applicant]
Wangjiang Zhu, Shuang Liang, Yichen Wei, and Jian Sun. Saliency optimization from robust background detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2814-2821, 2014. [cited by applicant]
X. Bai and G. Sapiro. Geodesic matting: A framework for fast interactive image and video segmentation and matting. International Journal of Computer Vision, 82(2):113-132, 2008. [cited by applicant]
Xiang Li, Tianhan Wei, Yau Pun Chen, Yu-Wing Tai, and Chi-Keung Tang. Fss-1000: A 1000-class dataset for few-shot segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2… [cited by applicant]
Xiaoning Zhang, Tiantian Wang, Jinqing Qi, Huchuan Lu, and Gang Wang. Progressive attention guided recurrent network for salient object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Rec… [cited by applicant]
Xiaoyong Shen, Aaron Hertzmann, Jiaya Jia, Sylvain Paris, Brian Price, Eli Shechtman, and Ian Sachs. Automatic portrait segmentation for image stylization. In Computer Graphics Forum, vol. 35, pp. 93-102. Wiley Online L… [cited by applicant]
Xu et al., Deep Interactive Object Selection, Mar. 13, 2016 arXiv:1603.04042v1 [cs.CV], pp. 1-9. (Year: 2016). [cited by applicant]
Y. Hu, A. Soltoggio, R. Lock, and S. Carter. A fully convolutional two-stream fusion network for interactive image segmentation. Neural Networks, 109:31-42, 2019. [cited by applicant]
Y. Li, J. Sun, C.-K. Tang, and H.-Y. Shum. Lazy snapping. In ACM Transactions on Graphics, vol. 23, pp. 303-308, 2004. [cited by applicant]
Y. Y. Boykov and M.-P. Jolly. Interactive graph cuts for optimal, boundary & region segmentation of objects in n-d images. In Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, vol. … [cited by applicant]
Yang et al., Meticulous Object Segmentation, Dec. 13, 2020, available at https://arxiv.org/pdf/2012.07181.pdf. [cited by applicant]
Yi Zeng, Pingping Zhang, Jianming Zhang, Zhe Lin, and Huchuan Lu. Towards high-resolution salient object detection. In Proceedings of the IEEE International Conference on Computer Vision, pp. 7234-7243, 2019. [cited by applicant]
Yichen Wei, Fang Wen, Wangjiang Zhu, and Jian Sun. Geodesic saliency using background priors. In European conference on computer vision, pp. 29-42. Springer, 2012. [cited by applicant]
Youwei Pang, Xiaoqi Zhao, Lihe Zhang, and Huchuan Lu. Multi-scale interactive network for salient object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9413-9422, 20… [cited by applicant]
Z. Li, Q. Chen, and V. Koltun. Interactive image segmentation with latent diversity. In IEEE CVPR, pp. 577-585, 2018. [cited by applicant]
Z. Liu, X. Li, P. Luo, C. C. Loy, and X. Tang. Semantic image segmentation via deep parsing network. arXiv preprint arXiv:1509.02634, 2015. [cited by applicant]
Zhang et al. in U.S. Appl. No. 16/988,055, filed Aug. 7, 2020, entitled Generating an Image Mask for a Digital Image by Utilizing a Multi-Branch Masking Pipeline With Neural Networks. [cited by applicant]
Zhiming Luo, Akshaya Mishra, Andrew Achkar, Justin Eichel, Shaozi Li, and Pierre-Marc Jodoin. Non-local deep features for salient object detection. In Proceedings of the IEEE Conference on computer vision and pattern re… [cited by applicant]
U.S. Appl. No. 14/945,245, Sep. 21, 2017, Preinterview 1st Office Action. [cited by applicant]
U.S. Appl. No. 14/945,245, Nov. 1, 2017, 1st Action Office Action. [cited by applicant]
U.S. Appl. No. 14/945,245, Apr. 17, 2018, Office Action. [cited by applicant]
U.S. Appl. No. 14/945,245, Sep. 12, 2018, Notice of Allowance. [cited by applicant]
U.S. Appl. No. 16/216,739, Feb. 25, 2021, Preinterview 1st Office Action. [cited by applicant]
U.S. Appl. No. 16/216,739, Apr. 5, 2021, 1st Action Office Action. [cited by applicant]
U.S. Appl. No. 16/216,739, Sep. 13, 2021, Office Action. [cited by applicant]
U.S. Appl. No. 16/216,739, Dec. 23, 2021, Notice of Allowance. [cited by applicant]
U.S. Appl. No. 15/799,395, Mar. 14, 2019, Office Action. [cited by applicant]
U.S. Appl. No. 15/799,395, Jul. 12, 2019, Notice of Allowance. [cited by applicant]
U.S. Appl. No. 15/967,928, Dec. 10, 2020, Preinterview 1st Office Action. [cited by applicant]
U.S. Appl. No. 15/967,928, Apr. 2, 2021, 1st Action Office Action. [cited by applicant]
U.S. Appl. No. 15/967,928, May 13, 2021, Office Action. [cited by applicant]
U.S. Appl. No. 15/967,928, Sep. 29, 2021, Notice of Allowance. [cited by applicant]
U.S. Appl. No. 16/231,746, Feb. 18, 2021, Preinterview 1st Office Action. [cited by applicant]
U.S. Appl. No. 16/231,746, Jun. 11, 2021, 1st Action Office Action. [cited by applicant]
U.S. Appl. No. 16/231,746, Nov. 10, 2021, Notice of Allowance. [cited by applicant]
U.S. Appl. No. 16/988,408, Oct. 5, 2021, Preinterview 1st Office Action. [cited by applicant]
U.S. Appl. No. 16/988,408, Nov. 24, 2021, 1st Action Office Action. [cited by applicant]
U.S. Appl. No. 16/988,408, Jan. 5, 2022, Notice of Allowance. [cited by applicant]
U.S. Appl. No. 16/376,704, Dec. 29, 2021, Preinterview 1st Office Action. [cited by applicant]
U.S. Appl. No. 16/376,704, Feb. 7, 2022, 1st Action Office Action. [cited by applicant]
U.S. Appl. No. 16/376,704, Jun. 14, 2022, Office Action. [cited by applicant]
U.S. Appl. No. 16/376,704, Oct. 4, 2022, Notice of Allowance. [cited by applicant]
U.S. Appl. No. 17/126,986, Aug. 30, 2022, Office Action. [cited by applicant]
U.S. Appl. No. 17/126,986, Jan. 17, 2023, Notice of Allowance. [cited by applicant]
U.S. Appl. No. 17/660,361, Dec. 8, 2022, Office Action. [cited by applicant]
U.S. Appl. No. 17/660,361, Mar. 28, 2023, Notice of Allowance. [cited by applicant]
U.S. Appl. No. 17/200,525, Mar. 6, 2023, Office Action. [cited by applicant]
Office Action as received in CN application 201810886944.1 dated Dec. 29, 2023. [cited by applicant]
Zhang, Y., Li, X., Lin, M., Chiu, B., & Zhao, M. (2020). Deep-recursive residual network for image semantic segmentation. Neural computing and applications, 32, 12935-12947. [cited by applicant]
U.S. Appl. No. 17/200,525, Sep. 5, 2023, Notice of Allowance. [cited by applicant]
Office Action as received in CN application 201910967936.4 dated Jun. 5, 2024. [cited by applicant]
Chang, Yong et al. “Accurate pelvis and femur segmentation in hip CT with a novel patch-based refinement.” IEEE journal of biomedical and health informatics 23.3 (2018): 1192-1204. (Year: 2018). [cited by applicant]
Costea, Arthur Daniel, Andra Petrovai, and Sergiu Nedevschi. “Fusion scheme for semantic and instance-level segmentation.” 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018. (Ye… [cited by applicant]
Zhou, Peng, et al. “Deepstrip: High-resolution boundary refinement.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020. (Year: 2020). [cited by applicant]
U.S. App. No. 17/585,140, Mar. 27, 2024, Notice of Allowance. [cited by applicant]
U.S. Appl. No. 17/655,493, Nov. 4, 2024, Notice of Allowance. [cited by applicant]
U.S. Appl. No. 17/655,493, Jul. 18, 2024, Office Action. [cited by applicant]
U.S. Appl. No. 17/584,170, May 15, 2025, Office Action. [cited by applicant]
U.S. Appl. No. 18/161,666, Sep. 24, 2025, Office Action. [cited by applicant]
U.S. Appl. No. 17/584,170, Aug. 27, 2025, Office Action. [cited by applicant]
U.S. Appl. No. 17/584,170, Oct. 21, 2025, Office Action. [cited by applicant]
U.S. Appl. No. 18/161,666, Jan. 6, 2026, Notice of Allowance. [cited by applicant]
U.S. Appl. No. 17/584,170, Dec. 29, 2025, Notice of Allowance. [cited by applicant]