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 .
Response to Arguments
Applicants amendments have overcome the rejections under 35 U.S.C. 112(b). Due to these amendments the rejections of claims 1-7 have been withdrawn.
Applicant's arguments filed 10/30/2025 have been fully considered but they are not persuasive. Applicant argues that
First, Applicant respectfully points out that Liu cannot be considered as the prior art at all.
It should be noted that Liuawas officially published on July 14, 2022. However, the priority date of this application is May 19, 2022. Obviously, Liu is published later than the priority date and therefore should not be considered as the prior art.
Additionally, Hofmann (see paragraph [0055] of the specification of Hofmann) only discloses using a processor and hardware to implement similar elements. Hofmann just discloses a method of training a generative adversarial network for performing semantic segmentation of image, which aims at training the generator neural network. Hofmann does not refer to the specific simulation models in the present application at all.
In view of the above, claim 1 is patentable.
The examiner notes that this argument relies on argument that Liu in not prior art due to the priority date. However applicant may not rely on the priority application because no certified copy of the foreign priority application has been filed nor has a translation of a copy of the prority application been filed. As stated in the non final rejection mailed on 7/30/2025:
Applicant cannot rely upon the certified copy of the foreign priority application to overcome this rejection because a translation of said application has not been made of record in accordance with 37 CFR 1.55. When an English language translation of a non-English language foreign application is required, the translation must be that of the certified copy (of the foreign application as filed) submitted together with a statement that the translation of the certified copy is accurate. See MPEP §§ 215 and 216. The examiner further notes that no copy of the foreign application has been received at all.
If applicant wishes to rely on the priority date applicant must file the certified copy of the foreign application and an English language translation of a non-English language foreign application is required, the translation must be that of the certified copy (of the foreign application as filed) submitted together with a statement that the translation of the certified copy is accurate
The dependent claims 2 and 5-7 are not argued separately from claim 1. Since the arguments for claim 1 were not persuasive, the arguments for claims 2 and 5-7 are not persuasive.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in China on 5/19/2022. It is noted, however, that applicant has not filed a certified copy of the CN202210556615.7 application as required by 37 CFR 1.55.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
first module (note the structure and equivalents may be found in paragraphs 58 and 59
and associated function see paragraphs 13-25)
second module (note the structure and equivalents may be found in paragraphs 58 and 59 and associated function see paragraphs 27-32)
in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim(s) 1, 2, 5-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al “Using Simulated Training Data of Voxel-Level Generative Models to Improve 3D Neuron Reconstruction” July 14 2022 in view of Hofmann US 2021/0303925. (both previously cited)
Applicant cannot rely upon the certified copy of the foreign priority application to overcome this rejection because a translation of said application has not been made of record in accordance with 37 CFR 1.55. When an English language translation of a non-English language foreign application is required, the translation must be that of the certified copy (of the foreign application as filed) submitted together with a statement that the translation of the certified copy is accurate. See MPEP §§ 215 and 216. The examiner further notes that no copy of the foreign application has been received at all.
Re claim 1 Liu discloses
a simulation model (My) for a tree-structured image designed based on a small amount of expert knowledge configured to generate a coarse-grained tree-structured image (see section II first paragraph note simulation mode M uses prior knowledge of neuron images note that a first stage is used to generate an initial coarse estimate which will be refined by the second stage. See also Section 2 Part A note that the neurons are tree like)
and a second module comprising a generative network model (see section II part B note that a second stage uses a GAN ) based on a morphological loss function configured to learn a style of a real tree-structured image and adjust details of a simulated image (see section II first paragraph note that the “MP loss” used to refine the coarse simulated images created at the first stage see also section II part B);
wherein the simulation model (My) for tree-structured image designed based on a small amount of expert knowledge is constructed by summarizing prior knowledge of the real tree structured image is configured to generate a tree-structured simulated image and a segmentation label corresponding to the tree-structured simulated image (see section II first paragraph “The first stage applies a simulation model Mγ that employs prior knowledge of neuron images. There are two parts in such prior knowledge, namely internal and external features. The internal features describe the shape of a neuron, such as branch lengths, branch radii, and branching angles. The external features approximate image characteristics, such as foreground and background intensity distributions, image smoothness, and noise patterns. We use internal features to generate neuron masks or segmentation labels and external features to convert the binary masks into grayscale images.” Note that segmentation label and simulated grey scale image are generated see also section II part a note that the output of stage is a simulated image and a label)
wherein prior knowledge of the tree-structured image comprises: morphologies of the tree (see section II part A 1 internal features note neuron morphologies are used to model a tree structure ), histograms of pixel or voxel intensity, background noise (see section II a external features “Then we also add round bright spots with random sizes at random positions on the background to simulate blob-like noise signals that are common in neuron images” note that a blob noise signal is used ), and blurring effect features of a branch (see section II part A 2 External features note that blur of the mask is an external feature );
and wherein the generative network model based on the morphological loss function is configured to keep an underlying segmentation label of an image unchanged ( See section II B 2 “For example, neuron branches in the refined image may appear significantly thinner or thicker than their underlying morphological models. A remedy to this problem is adding a loss that preserves the general label-intensity correspondence for each voxel” note that label correspondence is preserved see also Section II B 1 note that Lc preserves the input segmentation labels), learn the style and morphological features of the real tree structured image (see section II B 1 Adversarial loss “The objective function of optimizing the refiner R , has two terms, the adversarial objective function for the refiner to learn the real image style and the expectation of Lc for preserving the input segmentation labels.” Note that real image style is learned by the refiner ), and adjust detail textures and an overall pixel or voxel distribution style of the simulated image ( See section II B first paragraph “A draft stack from the first stage should be further refined at the second stage before it becomes good enough to serve as training data. The refining process is learned from unlabeled real data by adversarial learning, which can be done by the classical GAN that consists of a refiner R and a discriminator D . We add the MP loss to the loss function of the refiner and call the revision MPGAN to highlight the advantage of preserving low-level segmentation label” note that simulated image is refined to look more like a real one while preserving the label data).
Liu does not expressly disclose a first module or a second module i.e a processor/hardware to implement the first and second module. Hofmann discloses using a processor and hardware to implement similar elements (See paragraph 55). One of ordinary skill in the art could have easily implemented the function first and second module using a processor and the results would be the same just implementing the method with a computer. The elements are simply performing the same function separately as the function of Liu is unchanged and merely implemented by hardware elements i.e a processor as described in Hofmann. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Liu and Hofmann.
Re claim 2 wherein in the first module, the simulation model for the tree-structured image designed based on a small amount of expert knowledge divides features of the tree-structured image into internal features and external features (see section II A note that external and internal features are used );
and wherein the internal features comprise geometric image features of the branch in the image (see section II A first paragraph note internal features represent Geometry of the object. ), comprising a depth of the tree (see section II A 1 Internal features note that one of the features is depth of tree), a bifurcation degree of the branch(see section II A 1 Internal features note that one of the features is branching degree), a radius of the branch (see section II A 1 Internal features note that one of the features is radius), a length of the branch (see section II A 1 Internal features note that one of the features is Branch length), and a direction of the branch (see section II A 1 Internal features note that one of the features is branching angle),
and the external features comprise texture features of the image (see section II A first paragraph “External features are referred to as representative intensity patterns of an image, such as spatial frequencies” note that spatial features could be considered texture features), an intensity distribution of the branch (see section II A 2 External features note that foreground intensity distribution is determined) and a noise distribution (see section II A first paragraph note that external feaures include noise distribution).
Re claim 5 Liu further discloses wherein in the second module, the underlying segmentation label of the image is kept unchanged (See section II B 2 “For example, neuron branches in the refined image may appear significantly thinner or thicker than their underlying morphological models. A remedy to this problem is adding a loss that preserves the general label-intensity correspondence for each voxel” note that label correspondence is preserved see also Section II B 1 note that Lc preserves the input segmentation labels), the style of the real tree structured image is learnt to adjust the simulated image (see section II B 1 Adversarial loss “The objective function of optimizing the refiner R , has two terms, the adversarial objective function for the refiner to learn the real image style and the expectation of Lc for preserving the input segmentation labels.” Note that real image style is learned by the refiner ),
inputting a simulated image z from the simulation model My to a refiner to generate a refined image R(z), and the real tree structured image x and the refined image R(z) are fed into a discriminator configured to learn to distinguish the real tree structured image from the refined image; and the discriminator and the refiner are optimized by following formulas;
PNG
media_image1.png
60
328
media_image1.png
Greyscale
where x represents sourced from a real image distribution preal, z is sourced from a simulated image distribution psim in the first stage; whereing objective functions of the refiner comprise two items: one is an adversarial objective function used by the refiner to learn the style of the real tree structured image, and the other is L controlling a change of a segmentation label of an input image . (see section II B 1 Adversrial loss note that the equations claimed here are disclosed in the section )
Re claim 6 Liu furher discloses with the objective function L in the refiner to control the change of the segmentation label of the input image, controlling a similarity between a generated image and an overall distribution of an original image satisfies:
PNG
media_image2.png
32
392
media_image2.png
Greyscale
where z represents the simulated image, and R (z) represents an image generated by the refiner, Lsim represents an image similarity loss designed for external features of the image, Lp represents an image morphology preserving loss designed for internal features of the image, and alpha and beta represent hyperparameters for balancing each loss function (see section II A 2 Data control loss note the exact same equation is used here in equation 4 along with the associated text)
Re claim 7 Liu disclseos wherein the image similarity loss is loss function in an unsupervised generative learning model, and is configured to control stability of a generative model during training and to avoid the generative model falling into a single mapping mode; and wherein a data similarity loss is as follows:
PNG
media_image3.png
362
488
media_image3.png
Greyscale
PNG
media_image4.png
276
524
media_image4.png
Greyscale
See section II A 2 Note that these equations correspond to equations 5-10 and these associated descriptive text..
Allowable Subject Matter
Claim 3 and 4 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN T MOTSINGER whose telephone number is (571)270-1237. The examiner can normally be reached 9AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SEAN T MOTSINGER/Primary Examiner, Art Unit 2673
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