DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Amendments
Applicant’s Amendment filed on 3/23/2026 has been entered and made of record.
Currently Pending claims: 1-2 and 4-17
Independent claim: 1
Amended claims: 1-2, 4, 7-8, and 12-15
Cancelled claim: 3
Response to Arguments
This office action is responsive to Applicant’s Arguments/Remarks Made in an
Amendment received on 3/23/2026.
Applicant’s arguments regarding objections to the drawings, specification, and claims, regarding rejections under 35 U.S.C. §112(b) with respect to claim 15, and regarding rejections under 35 U.S.C. §101 with respect to claims 1-17, see pages 6-9, filed on 3/23/2026, have been fully considered and are persuasive. The rejections under 35 U.S.C. §112(b) and 35 U.S.C. §101 have been withdrawn. The objections to the drawings, specifications, and claims have also been withdrawn.
Applicant’s arguments regarding rejections under 35 U.S.C §102 with respect to claims 1, 3, 5, 8-10, and 12-17, see pages 9-10, filed on 3/23/2026, have been fully considered and are not persuasive. Applicant on pages 9-10 argues stating:
“In claim 1, the output of the trained machine-learning model is evaluated according to a quality criterion, and a feedback signal is provided for adapting the parameters of the image processing workflow based on the evaluation. This feature is not disclosed in Cruz … There is no disclosure in Cruz that a feedback signal generated based on the evaluation is used to adapt the original Conventional Image Processing (CIP) workflow. In Cruz, the CIP is used to generate the initial set of weakly labeled data, but the feedback loop in Cruz is for the creation of a new, more refined machine learning model, not for the adaptation of the parameters of the original CIP pipeline.”
The Examiner respectfully disagrees. Cruz discloses a method of training a CIP-based deep learning network (CDL), evaluating the CDL, and using the evaluation to train a manually based deep learning network (MDL). In the caption for Figure 1(a) on Page 3, Cruz discloses “General workflow of the proposed pipeline, part 1: (In blue, CIP-based DL: CDL)” and “Proposed pipeline, part 2: (In green, for Manually based DL: MDL)”. Furthermore, on page 8 line 1 of the Specification of the instant application, it is disclosed that the image processing workflow may comprise “one or more machine-learning-based image processing steps”. Together, the Examiner considers this to indicate that all steps from training the CDL to training the MDL can be considered as part of a single “image processing workflow”. Referring to Fig. 1 and 2 below (which correspond to Figure 1(a) from Cruz, modified with labels by the Examiner, and Figure 5 from the instant application, respectively), the input box in Fig. 1 corresponds to label 510 and the output box to label 520. The feedback box and step 1.3 corresponds to label 580. Step 1.2 encompasses labels 530-570. As the pipeline in Cruz includes the entire training process from CDL to MDL, steps 1.2-2.2 fit within label 500. The evaluation and feedback loop in Cruz, as noted by the Applicant, is used to train the MDL model, which inherently involves adapting the parameters of the model. Since the MDL model is considered to be part of the same image processing workflow and thus its parameters a part of the workflow, Cruz therefore discloses evaluating an output of the machine-learning model according to a quality criterion and providing a feedback signal for adapting parameters of the image processing workflow based on the evaluation. Please note that the amended claim 1 now also includes limitations recited in the original claim 3, which was also rejected using Cruz. Therefore, claim 1 is now rejected accordingly. Please note that amended claims 8 and 15 have been rejected using the same reference cited previously, however, as necessitated by amendment.
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Fig. 1 corresponding to Figure 1(a) from Cruz with additional labels added by the Examiner.
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Fig. 2 corresponding to Figure 5 from the instant application.
Applicant’s arguments regarding rejections under 35 U.S.C §103 with respect to claims 2, 4, 6-7, and 11, see pages 10-12, filed on 3/23/2026, have been fully considered and are not persuasive. Applicant on pages 11 argues stating:
“As discussed above, the main reference Cruz fails to disclose or teach evaluating an output of the machine-learning model according to a quality criterion and providing a feedback signal for adapting parameters of the image processing workflow based on the evaluation. This gap is not filled by any of the cited references Sharma, Shiraishi, Park, or Hillen.”
As the Applicant’s argument is dependent on Cruz not anticipating amended claim 1, the Examiner responds to these arguments by referring the response to the 35 U.S.C. §102 rejection above.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 5, 8-10, and 12-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cruz et al. (Garcia Santa Cruz, Beatriz, et al. "Generalising from conventional pipelines using deep learning in high-throughput screening workflows." Scientific Reports 12.1 (2022): 11465) (hereafter, "Cruz").
Regarding claim 1, Cruz discloses A method for training a machine-learning model for use in a scientific or surgical imaging system, the method comprising: obtaining a plurality of images of a scientific or surgical imaging system, for use as training input images (See page 3 section titled Methods, subsection on page 4 titled hiPSC generation and imaging acquisition, lines 4-6, Confocal images were obtained with an Opera QEHS spinning disk microscope (Perkin Elmer) under a 60x water immersion objective); obtaining a plurality of training outputs (See page 3 Figure 1(a), Steps 1.1 Weakly labeled masks; See page 6 section titled Results, lines 1-2, the generalisation capabilities of a DL network trained on noisy label data generated by a CIP pipeline) that are based on the plurality of training input images and that are based on an image processing workflow of the scientific or surgical imaging system, the image processing workflow comprising a plurality of image processing steps (See page 3 section titled Methods, subsection on page 4 titled CIP pipeline, lines 4-6, Examples of techniques used as building blocks to create such pipelines are: image deconvolution, thresholding Gaussian filtering, Top-hat filtering, watershed transformation, difference of Gaussians, Butterworth filter or high pass filter); and training the machine-learning model using the plurality of training input images and the plurality of training outputs (See page 3 Figure 1(a), Steps 1.1-1.2, captions [1.1] First a weakly labelled dataset is created using conventional imaging processing (CIP). [1.2] After that, a U-net like architecture is trained); evaluating an output of the machine-learning model (See page 3 Figure 1(a), steps 1.3, caption [1.3] the accuracy of the [predicted mask] evaluated; See page 3 section titled Methods, subsection on page 3-4 titled Pipeline setup, lines 8-9, Using the GUI tool, the experts can also correct the potential inaccuracies of the images) according to a quality criterion (See page 3 section titled Methods, page 4 subsection titled Part A: Measuring the DL generalization…, page 5 sub-subsection titled Evaluation, lines 6-8, (1) a qualitative analysis using blind expert ranking. (2) a quantitative analysis using Bounding Boxes (BB) as a surrogate metric. (3) a qualitative analysis using the overlapping segmentation using dice-coefficient employing manually corrected samples); and providing a feedback signal for adapting one or more parameters of the image processing workflow based on the evaluation of the output of the machine-learning model (See page 3 section titled Methods, page 4 subsection titled Part A: Measuring…, page 5 sub-subsection titled Evaluation: qualitative analysis by expert rating, lines 2-4, The expert quantification was performed in RGB microscopy images with adjacent plots of two label masks of that image as well as an overlay of the image with the masks. The masks were either produced by CIP or CDL [machine learning model] and the experts were blinded about the randomized order of the segmentation. They scored each labelling from 1 (worst quality) to 10 (best quality). The score is considered as a feedback signal).
Regarding claim 5, Cruz discloses the method according to claim 1, wherein the machine-learning model is trained, using supervised learning (Page 3, Figure 1(a) captions, [1.2] After that, a U-net like architecture is trained. Here U-net is trained via supervised learning using the input images and the weakly labeled masks), to transform an image of the scientific or surgical imaging system into an output, by applying the plurality of training input images at an input of the machine-learning model (See page 3 Figure 1(a), Steps 1.1-1.2, captions [1.1] First a weakly labelled dataset is created using conventional imaging processing (CIP)) and using the plurality of training outputs as desired output during training of the machine-learning model (Page 3, §Pipeline setup. Labels are automatically generated using CIP techniques; (2) These masks are employed to conduct a supervised train using CNN. Examiner considers the labels and masks as “training outputs”).
Regarding claim 8, Cruz discloses a method for training a machine-learning model (See page 4, subsection training, The network was trained in MATLAB) for use in a scientific or surgical imaging system (Page 3, first paragraph, This dataset includes imaging samples of the autophagy pathway using the Rosella pH-sensitive biosensor using human iPS cells), the method comprising: generating a plurality of images based on imaging sensor data of an optical imaging sensor of the scientific or surgical imaging system (See page 3 section titled Methods, subsection on page 4 titled hiPSC generation and imaging acquisition, lines 4-6, Confocal images were obtained with an Opera QEHS spinning disk microscope (Perkin Elmer) under a 60x water immersion objective); generating, using an image processing workflow of the scientific or surgical imaging system, a plurality of outputs based on the plurality of images (See page 3 Figure 1(a), Steps 1.1 Weakly labeled masks; See page 6 section titled Results, lines 1-2, the generalisation capabilities of a DL network trained on noisy label data generated by a CIP pipeline), the image processing workflow comprising a plurality of image processing steps (See page 3 section titled Methods, subsection on page 4 titled CIP pipeline, lines 4-6, Examples of techniques used as building blocks to create such pipelines are: image deconvolution, thresholding Gaussian filtering, Top-hat filtering, watershed transformation, difference of Gaussians, Butterworth filter or high pass filter); and providing the plurality of images as training input images and the plurality of outputs as training outputs for training a machine-learning model according (See page 3 Figure 1(a), Steps 1.1-1.2, captions [1.1] First a weakly labelled dataset is created using conventional imaging processing (CIP). [1.2] After that, a U-net like architecture is trained) to the method of claim 1.
Regarding claim 9, Cruz discloses the method according to claim 8, further comprising obtaining the trained machine-learning model and replacing the image processing workflow with the machine-learning model (See page 3 section titled Methods, page 4 subsection titled Part A: Measuring…, page 4-5 sub-subsection titled User-friendly GUI, lines 1-5, Next, the CNN was integrated into a user-friendly tool using the MATLAB Image Label App. This integration allows easy handling for the potential users, biological researchers that often do not have experience with programming. By integrating the CNN as a segmentation algorithm into the image label app the tool does not only allow for an easy prediction of the mask using the CNN solution but also an intuitive way to correct the errors of the mask using manual segmentation tools) that is trained according to the method of claim 1.
Regarding claim 10, Cruz discloses the method according to claim 8, further comprising training the machine-learning model using the method of claim 1 (See page 3 Figure 1(a), Step 1.2, caption [1.2] After that, a U-net like architecture is trained).
Regarding claim 12, Cruz discloses the method according to claim 8, wherein the image processing workflow comprises at least one of one or more deterministic image processing steps (See page 3 section titled Methods, subsection on page 4 titled CIP pipeline, lines 4-6, Examples of techniques used as building blocks to create such pipelines are: image deconvolution, thresholding Gaussian filtering, Top-hat filtering, watershed transformation, difference of Gaussians, Butterworth filter or high pass filter. The claim only requires “at least one of”), one or more image processing steps with an iterative optimization component, or one or more machine-Learning-based image processing steps.
Regarding claim 13, Cruz discloses a system comprising one or more processors and one or more storage devices, wherein the system is configured to perform the method of claim 1 (See page 3 section titled Methods, page 4 subsection titled Part A: Measuring…, page 4 sub-subsection titled Training, line 1, The network was trained in MATLAB. As the model was trained on a computer program running on a general-purpose computer, it must have had a processor and storage).
Regarding claim 14, Cruz discloses a system comprising one or more processors and one or more storage devices, wherein the system is configured to perform the method of claim 8 (See page 3 section titled Methods, page 4 subsection titled Part A: Measuring…, page 4 sub-subsection titled Training, line 1, The network was trained in MATLAB. As the model was trained on a computer program running on a general-purpose computer, it must have had a processor and storage).
Regarding claim 15, Cruz discloses a system comprising one or more processors and one or more storage devices (See page 3 section titled Methods, page 4 subsection titled Part A: Measuring…, page 4 sub-subsection titled Training, line 1, The network was trained in MATLAB. As the model was trained on a computer program running on a general-purpose computer, it must have had a processor and storage), wherein the system is configured to obtain an image based on imaging sensor data of an optical imaging sensor of the scientific or surgical imaging system (See page 3 section titled Methods, subsection on page 4 titled hiPSC generation and imaging acquisition, lines 4-6, Confocal images were obtained with an Opera QEHS spinning disk microscope (Perkin Elmer) under a 60x water immersion objective); process the image using a machine-learning model that is trained according to the method of claim 1 (See page 3 section titled Methods, page 4 subsection titled Part A: Measuring…, page 5 sub-subsection titled Manual correction, lines 1-2, Instead, the images were firstly predicted with our trained network) to generate an output of the machine-learning model (See page 3 section Methods, page 4 subsection Part A: Measuring…, page 5 sub-subsection Manual correction, the images were firstly predicted with our trained network and then manually corrected using the tools available in the GUI. Examiner considers using outputs in the GUI to imply generating an output).
Regarding claim 16, Cruz discloses a non-transitory computer-readable storage medium including a program code configured to perform (page 12 section titled Data and code availability, line 1, Code and data will be available thought the R3 platform of the University of Luxembourg. As the data and code are available for access, there must be a non-transitory CRM), when executed by a processor, the method according to claim 1.
Regarding claim 17, Cruz discloses a non-transitory computer-readable storage medium including a program code configured to perform, when executed by a processor (page 12 section titled Data and code availability, line 1, Code and data will be available thought the R3 platform of the University of Luxembourg. As the data and code are available for access, there must be a non-transitory CRM), the method according to claim 8.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Cruz in view of Sharma (US Patent Application Publication No. 20200104994).
Regarding claim 2, Cruz discloses the method according to claim 1.
Cruz fails to disclose further comprising obtaining the one or more input parameters of the image processing workflow as further training input and training the machine-learning model using the one or more input parameters as further training input.
However, Sharma discloses further comprising obtaining the one or more input parameters of the image processing workflow as further training input and training the machine-learning model using the one or more input parameters as further training input (Fig 2, #206; ¶0038, lines 1-5, At step 206, the machine learning model is trained based on the training images, input parameters, output interpretations determined for the training images, and ground truth interpretations associated with the training images).
Both Cruz and Sharma are analogous to the claimed invention because Cruz is in the field of scientific image processing and Sharma is in the field of medical image processing. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the use of input parameters in training the machine learning model from Sharma into machine learning based image processing workflow of Cruz. The suggestion/motivation for doing so would have been to optimize the relationship between pre-processing parameters and machine learning models (¶0038, lines 33-35, To learn which pre-processing algorithms and settings are best for various possible downstream AI image-analysis algorithms).
This method of improving Cruz was within the ordinary ability of one of ordinary skill in the art based on the teachings of Sharma.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Cruz with the teachings of Sharma to obtain the invention as specified in claim 2.
Claims 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Cruz in view of Shiraishi (US Patent Application Publication No. 20220189149).
Regarding claim 4, Cruz discloses the method according to claim 1.
Cruz fails to disclose wherein the machine-learning model is trained to generate the feedback signal for adapting one or more parameters of the image processing workflow (Note that Cruz discloses feedback signals from human evaluators but not from the machine-learning model itself).
However, Shiraishi discloses wherein the machine-learning model is trained to generate the feedback signal for adapting one or more parameters of the image processing workflow (Figure 26, #155, 156 Evaluation model and unit; ¶0121, lines 7-9, The evaluation model 156 is a machine learning model that outputs the evaluation result 55 in a case where the output image 42 is input).
Both Cruz and Shiraishi are analogous to the claimed invention because Cruz and Shiraishi are in the field of scientific image processing. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the training of the machine learning model to evaluate the output from Shiraishi into machine learning based image processing workflow of Cruz. The suggestion/motivation for doing so would have been to be able to omit additional processing for evaluating the output (¶0121, lines 12-13, By doing so, it is possible to omit the processing of calculating the image feature value).
This method of improving Cruz was within the ordinary ability of one of ordinary skill in the art based on the teachings of Shiraishi.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Cruz with the teachings of Shiraishi to obtain the invention as specified in claim 4.
Regarding claim 11, Cruz discloses the method according to claim 8, further comprising obtaining a feedback signal, the feedback signal being based on the training of the machine-learning model or based on an output of the trained machine-learning model when the machine-learning model is used by the surgical or scientific imaging system (See page 3 section titled Methods, page 4 subsection titled Part A: Measuring…, page 5 sub-subsection titled Evaluation: qualitative analysis by expert rating, lines 2-4, The expert quantification was performed in RGB microscopy images with adjacent plots of two label masks of that image as well as an overlay of the image with the masks. The masks were either produced by CIP or CDL [machine learning model] and the experts were blinded about the randomized order of the segmentation. They scored each labelling from 1 (worst quality) to 10 (best quality). The score is considered as a feedback signal).
Cruz fails to disclose using the feedback signal as input to the image processing workflow or to the trained machine-learning model.
However, Shiraishi discloses using the feedback signal as input to the image processing workflow or to the trained machine-learning model (¶0117, lines 1-6, The update unit 148 updates the trained model 41 according to the evaluation result from the accuracy evaluation unit 147. For example, the update unit 148 changes values of various parameters of the trained model 41 by a stochastic gradient descent method or the like accompanied by a learning coefficient).
Both Cruz and Shiraishi are analogous to the claimed invention because Cruz and Shiraishi are in the field of scientific image processing. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the use of the feedback signal from Shiraishi into machine learning based image processing workflow of Cruz. The suggestion/motivation for doing so would have been to improve the discrimination accuracy of the model (¶0120, lines 5-6, Accordingly, the discrimination accuracy of the class of the trained model 41 can be easily improved).
This method of improving Cruz was within the ordinary ability of one of ordinary skill in the art based on the teachings of Shiraishi.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Cruz with the teachings of Shiraishi to obtain the invention as specified in claim 11.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Cruz in view of Park et al. (US Patent Application Publication No. 20220183645) (hereafter, "Park").
Regarding claim 6, Cruz discloses the method according to claim 1.
Cruz fails to disclose wherein the machine-learning model is trained, using reinforcement learning, to transform an image of the scientific or surgical imaging system into an output, wherein a difference between the output of the machine-learning model during training and a training output of the plurality of training outputs is used to determine a reward during the reinforcement learning-based training.
However, Park discloses wherein the machine-learning model is trained, using reinforcement learning, to transform an image of the scientific or surgical imaging system into an output (¶0061, lines 1-4, The agent unit 310 may train an agent to have an appropriate color conversion by allowing a color conversion image generated based on a result of the reinforcement learning), wherein a difference between the output of the machine-learning model during training and a training output of the plurality of training outputs is used to determine a reward during the reinforcement learning-based training (¶0064, lines 15-19, a difference between the reconstructed image and a result passing through image preprocessing such as a morphology operation, and the like, is defined to be an error, and a reciprocal of the error is used as a reward of a deep Q-network).
Both Cruz and Park are analogous to the claimed invention because Cruz is in the field of scientific image processing and Park is in the field of medical image processing. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the reinforcement learning of Park into machine learning image processing workflow of Cruz. The suggestion/motivation for doing so would have been the substitution of a reinforcement learning model in place of the deep-learning model of Cruz would have been obvious to a person of ordinary skill in the art (¶0040, lines 12-13, perform training of the image data through deep learning of artificial intelligence, and the like. Park suggests the applicability of deep learning as well as similar algorithms).
This method of improving Cruz was within the ordinary ability of one of ordinary skill in the art based on the teachings of Park.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Cruz with the teachings of Park to obtain the invention as specified in claim 6.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Cruz in view of Hillen (US Patent Application Publication No. 20190313963).
Regarding claim 7, Cruz discloses the method according to claim 1.
Cruz fails to disclose wherein the machine-learning model is trained, as a generator model of a pair of generative adversarial networks to transform an image of the scientific or surgical imaging system into an output, with a discriminator model of the pair of generative adversarial networks being trained based on the plurality of training outputs.
However, Hillen discloses wherein the machine-learning model is trained, as a generator model of a pair of generative adversarial networks to transform an image of the scientific or surgical imaging system into an output (¶0033, lines 71-79, For example, the machine learning inference 412 and machine learning trainer 408 can use neural networks such as a generative adversarial networks (GANs) in its machine learning architecture (e.g., an unsupervised machine learning architecture). In general, a GAN includes a generator neural network, a different specific x implementation kind of the Image Augmentation 506, that generates data (e.g., different versions of the same image by flips, inversions, mirroring etc.), with a discriminator model of the pair of generative adversarial networks being trained based on the plurality of training outputs (¶0033, lines 79-81, that is evaluated by a discriminator neural network, a specific type of the Neural Network Model 508, for authenticity (e.g., to identify the dental images).
Both Cruz and Hillen are analogous to the claimed invention because Cruz is in the field of scientific image processing and Hillen is in the field of medical image processing. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the generative adversarial network of Hillen into the machine learning based image processing workflow of Cruz. The suggestion/motivation for doing so would have been the substitution of a generative adversarial network model in place of the deep-learning model of Cruz would have been obvious to a person of ordinary skill in the art (¶0033, lines 4-5, supervised learning techniques may be implemented; ¶0033, lines 30-31, reinforcement learning techniques may also be used; ¶0033, lines 58-59, stochastic gradient descent. Hillen describes multiple different machine learning training options).
This method of improving Cruz was within the ordinary ability of one of ordinary skill in the art based on the teachings of Hillen.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Cruz with the teachings of Hillen to obtain the invention as specified in claim 7.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/XIAOMAO DING/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676