DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 10, 2026, has been entered.
Response to Amendment
Claims 1-3, 6-8, 10-11, and 13 were previously pending. Applicant’s amendment filed April 8, 2026, was not entered. Applicant’s amendment filed June 10, 2026, has been entered in full. Claims 1 and 11 are amended. No claims have been added or cancelled. Claims 1-3, 6-8, 10-11, and 13 remain pending.
Response to Arguments
Applicant traverses the previous written description and enablement rejections under 35 U.S.C. 112(a) (Remarks filed June 10, 2026, hereinafter Remarks: Pages 6-17). Applicant’s arguments are substantially the same as those made at pages 6-17 of the remarks filed April 8, 2026. Examiner already provided a response to these arguments in the Advisory Action mailed April 16, 2026. The response in the Advisory Action is maintained.
Applicant argues that the amendments to the claims overcome the previous indefiniteness rejections under 35 U.S.C. 112(b) (Remarks: Page 18). Examiner agrees. The previous rejections under 35 U.S.C. 112(b) are withdrawn.
Applicant traverses the previous rejection of claim 11 under 35 U.S.C. 101, noting that the claim has been amended to recite “one or more computers” (Remarks: Page 18). Examiner agrees that the recitation of the computers excludes non-statutory embodiments from the scope of the claim and overcomes the previous rejection under 35 U.S.C. 101, which is withdrawn. Examiner also notes that the recited computers are sufficiently structural to preclude the previous interpretation of the “data processing system …” under 35 U.S.C. 112(f), which is also withdrawn.
Applicant disagrees with arguments made by Examiner in the Advisory Action dated April 16, 2026 (Remarks: Pages 18-21).
Regarding Ex Parte Kirti, Applicant argues that although it is non-precedential, the decision may yet be persuasive (Remarks: Page 19). Applicant further argues that although Ex Parte Kirti involves a completely different type of machine learning model, it still presents a similar ‘112(a) issue and thus should persuade Examiner that the “black box” description of the claimed neural network is adequate (Remarks: Pages 19-20).
Examiner respectfully disagrees. “The inquiry into whether the description requirement is met must be determined on a case-by-case basis and is a question of fact.” MPEP 2163.04, citing to In re Wertheim, 541 F.2d 257, 262, 191 USPQ 90, 96 (CCPA 1976). That the Board found a “black box” description of a different type of neural network to be adequate in a different case with different facts is not necessarily persuasive that the “black box” description of the presently claimed neural network should also be considered adequate in view of the specific facts developed on the record of the instant application.
Some of the facts specific to the instant application are that the ‘Staib’ prior art (“Boundary Fitting with Parametrically Deformable models,” 1992) discloses elliptic Fourier descriptors, that the ‘Xu’ prior art (“Explicit Shape Encoding for Real-Time Instance Segmentation,” 2019) teaches estimating non-elliptic Fourier descriptors, and yet Applicant has argued that it would “go[] beyond a simple routine substitution” to modify Xu to use an elliptic Fourier descriptor rather than a non-elliptic Fourier descriptor (Remarks filed November 21, 2025: Page 16, last par.).
Applicant argues that Examiner improperly conflates Applicant’s positions with respect to § 103 and § 112(a) (Remarks: Pages 20-21). Examiner respectfully disagrees. While Applicant’s arguments were made with respect to rejections under § 103, they do not have to be read completely apart from their arguments with respect to § 112(a). The question at the heart of the enablement test under 35 U.S.C. 112(a) is “is the experimentation needed to practice the invention undue or unreasonable?” MPEP 2164.01. Whether a modification to the prior art necessary to practice the invention “goes beyond a simple routine substitution” is clearly relevant to this question. Such facts are also pertinent to determining whether defining the model output as an elliptical Fourier descriptor is sufficient to provide an adequate written description, as Applicant has argued in relation to Ex Parte Kirti (e.g., Remarks at 7 and 9).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3, 6-8, 10-11, and 13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “inputting the image to a trained neural network, and estimating, by the trained neural network, a representation of a segmentation contour of an object in the image” (reference characters omitted). Additional limitations of claim 1 further define the contour and the estimated representation of the contour.
The estimating limitation is functional at least because it describes a function of estimating a representation of a segmentation contour of an object in an image. The estimating limitation is also computer-implemented at least because it requires a “neural network”, which is computer-implemented.
MPEP 2161.01, Subsection I, provides instructions for determining whether there is adequate written description for a computer-implemented functional claim limitation, including the following:
“Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.”
“When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may "express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure." Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340, 86 USPQ2d 1609, 1623 (Fed. Cir. 2008) (internal citation omitted). It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement.”
Examiner has searched the specification for an explanation of how the estimating step is performed and has identified the following sections as most-pertinent. All citations are to the published application – i.e., US 2023/0298181 A1 – unless otherwise noted.
PNG
media_image1.png
200
400
media_image1.png
Greyscale
PNG
media_image2.png
200
400
media_image2.png
Greyscale
PNG
media_image3.png
200
400
media_image3.png
Greyscale
PNG
media_image4.png
200
400
media_image4.png
Greyscale
PNG
media_image5.png
200
400
media_image5.png
Greyscale
PNG
media_image6.png
200
400
media_image6.png
Greyscale
PNG
media_image7.png
200
400
media_image7.png
Greyscale
PNG
media_image8.png
200
400
media_image8.png
Greyscale
As can be seen from the portions reproduced above, the specification generally restates the desired result of estimating a representation of a segmentation contour of an object in the image using a trained machine learning system without providing any explanation of how this is performed. For example, Figs. 1-4 and pars. [0049]-[0050] and [0054] merely state that a neural network estimates the representation. The neural network is treated as a “black box” and there is no explanation of how the neural network makes such an estimation. For example, what is the architecture of the neural network? I.e., what types of layers does it use and how are they arranged? What type of learning (e.g., supervised, unsupervised, adversarial, etc.) was used to train the neural network? What data sources and loss function(s) were used to train the neural network? What sequence of processing steps is performed by the neural network to produce the segmentation contour representation from the input image? None of these details are provided by the specification.
Given this lack of detail regarding the structure and functioning of the neural network in the specification, one of ordinary skill in the art would not know how the inventor intends to achieve the claimed function of estimating, by a trained machine learning system, a representation of a segmentation contour of an object in an image and therefore could not reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. It would not be enough if one skilled in the art could hypothetically design and train a neural network to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement.
Therefore, claim 1 fails to comply with the written description requirement of 35 U.S.C. 112(a).
Claim 11 recites substantially similar limitations and therefore also fails to comply with the written description requirement of 35 U.S.C. 112(a) for substantially the same reasons. Claims 2-3, 6-8, 10, and 13 also fail to comply with the written description requirement of 35 U.S.C. 112(a) at least because they depend from and include the limitations of claim 1.
Claim 7 further recites that “a visibility score is generated by the trained neural network” and further fails to comply with the written description requirement of 35 U.S.C. 112(a) for substantially the same reason as claim 1. I.e., given the general lack of detail regarding the neural network described in the specification, one of ordinary skill in the art could not reasonably conclude that the inventor possessed an invention producing further visibility score outputs from that neural network as required by claim 7.
Claim 8 depends from claim 7 and therefore also further fails to comply with the written description requirement of 35 U.S.C. 112(a) for substantially the same reason as claim 7.
Claims 1-3, 6-8, 10-11, and 13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
To satisfy the enablement requirement of 35 U.S.C. 112(a), the specification must teach those skilled in the art how to make and use the full scope of the claimed invention without “undue experimentation.”
Claim 1 requires, among other elements, estimating, by a trained neural network, a representation of a segmentation contour of an object in an image. In order to make the invention, one of ordinary skill in the art would have to train a neural network so that it can estimate a representation of a segmentation contour of an object in an image (i.e., perform training). In order to use the invention, one of ordinary skill in the art would have to operate the trained neural network on a given input image (i.e., perform inference).
As discussed above with respect to the written description requirement of 35 U.S.C. 112(a), the specification generally discloses a neural network that performs the estimation, but generally treats the neural network as a “black box” and does not disclose details about the network’s structure, training, or operation. This lack of detail would leave one of ordinary skill in the art with several questions that would have to be answered by experimentation in order to make and use the trained machine learning system required by the claimed invention. For example, what architecture neural network should be used? I.e., what types of layers should be included in the neural network and how should they be arranged? The answers to these questions would affect both training and inference. What type of learning (e.g., supervised, unsupervised, adversarial, etc.) should be used to train the neural network? And what data sources and loss function(s) should be used to train the neural network? The answers to these questions would primarily affect training.
As explained in MPEP 2164.01(a), factors to be considered when assessing whether any necessary experimentation required by the specification is “reasonable” or is “undue” include:
(A) The breadth of the claims;
(B) The nature of the invention;
(C) The state of the prior art;
(D) The level of one of ordinary skill;
(E) The level of predictability in the art;
(F) The amount of direction provided by the inventor;
(G) The existence of working examples; and
(H) The quantity of experimentation needed to make or use the invention based on the content of the disclosure.
Regarding factor (A) The breadth of the claims, the claims cover using any type of neural network in any way that achieves the desired outcome of estimating a representation of a segmentation contour. As discussed above, extensive experimentation would be needed to select an architecture, a training type, a training dataset, and loss functions to make and use a neural network that performs the claimed representation estimation. This factor weighs toward a need for undue experimentation to make and use the claimed invention.
Regarding factors (B) The nature of the invention and (C) The state of the prior art, the invention is focused on the instance segmentation problem recognized within the art of image analysis. See, e.g., par. [0002] of the published specification. As demonstrated by the ‘Hafiz’ (“A survey on instance segmentation: state of the art,” 3 July 2020) reference, many different approaches for performing instance segmentation using a trained machine learning system were known in the prior art. For example, Figures 2-5 and 7-12 of Hafiz illustrate many different neural network architectures used for instance segmentation. In another example, Section 4 lists multiple different datasets used to train machine learning for instance segmentation, each dataset containing images of different types of objects/scenes, under different conditions (e.g., weather) and captured using different types of devices. The high variety of different architectures and datasets shown in Hafiz indicates that a large amount of experimentation would be required to select an appropriate architecture and training dataset for making and using the invention as claimed. These factors weigh toward a need for undue experimentation to make and use the claimed invention.
Furthermore, none of the various prior art examples discussed by Hafiz performs instance segmentation by estimating a representation of a segmentation contour in the manner required by the claims. The techniques taught by Hafiz generally include mask-based techniques, sliding window techniques, region-based techniques, and pixel labeling and clustering techniques (Sec. 2), none of which includes predicting a representation of a segmentation contour as claimed. I.e., none of the cited techniques predicts a Fourier descriptor. Additionally, ‘Benbarka’ (“FourierNet: Compact mask representation for instance segmentation using differentiable shape decoders,” 2020) and ‘Xu’ (“Explicit Shape Encoding for Real-Time Instance Segmentation,” 2019) are examples of prior art that describe estimating Fourier descriptors, but neither estimates elliptic Fourier descriptors. Instead, both estimate Fourier descriptors from a 1D signal of angularly-spaced radius measurements (Benbarka: Sec. 3.1; Xu: e.g., Sec. 3.3.1 and Sec. 3.2.3, Comparison with Other Fitting Methods). As Applicant has acknowledged, substituting elliptical Fourier descriptors for the non-elliptical 1D Fourier descriptors in Xu (and, similarly, Benbarka) would not be simple or routine (Remarks filed November 21, 2025: Page 16). Furthermore, Benbarka’s neural network does not further estimate at least one parameter of a geometric transformation (e.g., Sec. 3.2, 1st par.) as required by the claimed invention, so further experimentation would be required to modify Benbarka’s teachings to achieve the claimed invention. This evidence demonstrates that, as a departure from techniques typically used in the prior art, a relatively large amount of direction or guidance would be needed for one of ordinary skill in the art to make and use a trained neural network that estimates a representation of a segmentation contour as required by the claimed invention. See MPEP 2164.05(a). This further causes factors (B) and (C) to weigh toward a need for undue experimentation to make and use the claimed invention.
This is also pertinent to factors (D) The level of one of ordinary skill, (E) The level of predictability in the art, and (F) The amount of direction provided by the inventor. Because the claimed invention can be seen as a departure from the kinds of techniques known in the prior art as demonstrated by Hafiz, Xu, and Benbarka, one of skill in the art would have less knowledge regarding making and using the type of trained neural network required by the claimed invention. The level of predictability would also be lower because one skilled in the art would have fewer disclosed or known results from which to extrapolate to the claimed invention. One of ordinary skill in the art would also generally need a relatively high amount of direction from the inventor because the claimed invention differs from the types of techniques known in the prior art as demonstrated by Hafiz, Xu, and Benbarka. See MPEP 2164.03. As explained above, the specification provides very little direction and leaves several open questions that would have to be answered by experimentation in order to make and use the claimed invention. Accordingly, these factors weigh toward a need for undue experimentation to make and use the claimed invention.
Regarding factor (G) The existence of working examples, the specification does provide examples of some outputs at Figs. 5-9, but does not provide any example of the actual trained neural network or how it is used to estimate a representation of a segmentation contour. Giving an example of an output of a system, but not describing that system or how it works does not significantly reduce the amount of experimentation needed to make and use a system that can provide such outputs. This factor weighs toward a need for undue experimentation to make and use the claimed invention.
Regarding factor (H) The quantity of experimentation needed to make or use the invention based on the content of the disclosure, as discussed above, the content of the disclosure lacks many of the details needed to actually make and use a trained neural network according to the claimed invention. Many experiments would be required to resolve the many questions left unanswered by the specification. This factor weighs toward a need for undue experimentation to make and use the claimed invention.
Having considered the above factors, it is clear that undue experimentation would be required for one of ordinary skill in the art to make and/or use the claimed invention. Therefore, claim 1 fails to comply with the enablement requirement of 35 U.S.C. 112(a).
Claim 11 recites substantially similar limitations and therefore also fails to comply with the enablement requirement of 35 U.S.C. 112(a) for substantially the same reasons. Claims 2-3, 6-8, 10, and 13 also fail to comply with the enablement requirement of 35 U.S.C. 112(a) at least because they depend from and include the limitations of claim 1.
Claim 7 further recites that “a visibility score is generated by the trained neural network” and further fails to comply with the enablement requirement of 35 U.S.C. 112(a) for substantially the same reason as claim 1. I.e., given the general lack of detail regarding the neural network described in the specification, one of ordinary skill in the art could not make or use a trained neural network producing further visibility score outputs from that neural network as required by claim 7 without undue experimentation.
Claim 8 depends from claim 7 and therefore also further fails to comply with the enablement requirement of 35 U.S.C. 112(a) for substantially the same reason as claim 7.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEOFFREY E SUMMERS whose telephone number is (571)272-9915. The examiner can normally be reached Monday-Friday, 7:00 AM to 3:30 PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at (571) 272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/GEOFFREY E SUMMERS/Examiner, Art Unit 2669
Prosecution Insights