CTNF 18/771,929 CTNF 91293 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-20 are currently pending in U.S. Patent Application No. 18/771,929 and an Office action on the merits follows. Information Disclosure Statement The information disclosure statement(s) (IDS) submitted comply with the provisions of 37 CFR 1.97 and 1.98, and have been considered accordingly. Examiner does note however, with respect to NPL Citation No. 3 (all three of Applicant’s IDS have the same receipt date 03/19/2025) - Udupa et al. “Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto-contouring for radiation therapy planning” , that the publication date listed features only the year, 2022. MPEP § 609.04(a) describes Content Requirements for IDS, and with reference to 37 CFR 1.98(b), describes “The date of publication supplied must include at least the month and year of publication, except that the year of publication (without the month) will be accepted if the applicant points out in the information disclosure statement that the year of publication is sufficiently earlier than the effective U.S. filing date and any foreign priority date so that the particular month of publication is not in issue ” . Examiner understands the omitted month to be at issue, because it may resolve whether the publication falls within, or outside of, the 1-year grace period associated with Exceptions under 102(b)(1)(A or B). https://doi.org/10.1002/mp.15854 suggests a month of publication no earlier than July 14, 2022, whereas Applicant’s EFD is July 14, 2023. While not a requirement for information per se as described in MPEP 704.11(b) and with reference to 37 CFR 1.105, Examiner welcomes Applicant’s assistance in providing the associated publication date (Month and Day), particularly if Applicant is aware of any published versions that precede the 1-year grace period from Applicant’s 7/14/2023 EFD. Grace Period Prior Art MPEP 2153.01(a) includes the following instructions: AIA 35 U.S.C. 102(b)(1)(A) first provides that a disclosure which would otherwise qualify as prior art under AIA 35 U.S.C. 102(a)(1) may be excepted as prior art if the disclosure is made: (1) one year or less before the effective filing date of the claimed invention; and (2) by the inventor or a joint inventor. Thus, a disclosure that would otherwise qualify as prior art under AIA 35 U.S.C. 102(a)(1) will not be treated as prior art by Office personnel if the disclosure is made one year or less before the effective filing date of the claimed invention, and the evidence shows that the disclosure is by the inventor or a joint inventor. Office personnel will not apply a disclosure as prior art under AIA 35 U.S.C. 102(a)(1) if it is apparent from the disclosure itself that it is by the inventor or a joint inventor. Specifically, Office personnel will not apply a disclosure as prior art under AIA 35 U.S.C. 102(a)(1) if the disclosure: (1) was made one year or less before the effective filing date of the claimed invention; (2) names the inventor or a joint inventor as an author or an inventor; and (3) does not name additional persons as authors on a printed publication or joint inventors on a patent. This means that in circumstances where an application names additional persons as joint inventors relative to the persons named as authors in the publication (e.g., the application names as joint inventors A, B, and C, and the publication names as authors A and B), and the publication is one year or less before the effective filing date, it is apparent that the disclosure is a grace period inventor disclosure, and the publication would not be treated as prior art under AIA 35 U.S.C. 102(a)(1). If, however, the application names fewer joint inventors than a publication (e.g., the application names as joint inventors A and B, and the publication names as authors A, B and C), it would not be readily apparent from the publication that it is by the inventor (i.e., the inventive entity) or a joint inventor and the publication would be treated as prior art under AIA 35 U.S.C. 102(a)(1) . (emphasis added). The Office has provided a mechanism for filing an affidavit or declaration (under 37 CFR 1.130) to establish that a disclosure is not prior art under AIA 35 U.S.C. 102(a) due to an exception in AIA 35 U.S.C. 102(b). See MPEP § 717. In the situations in which it is not apparent from the prior disclosure or the patent application specification that the prior disclosure is by the inventor or a joint inventor, the applicant may establish by way of an affidavit or declaration that a grace period disclosure is not prior art under AIA 35 U.S.C. 102(a)(1) because the prior disclosure was by the inventor or a joint inventor. MPEP § 2155.01 discusses the use of affidavits or declarations to show that the prior disclosure was made by the inventor or a joint inventor under the exception of AIA 35 U.S.C. 102(b)(1)(A) for a grace period inventor disclosure. At least one reference cited below was published within the 1-year grace period before the effective filing date of the claimed invention and names at least one inventor as an author. However, the reference also names at least one additional author that is not an inventor of the instant application. Under such circumstances it is not readily apparent that the reference is by the inventor or a joint inventor, and therefore the reference must be treated as prior art. See above. If the reference does qualify as an exception under 35 U.S.C. 102(b)(1), Applicant may consider filing an affidavit or declaration as indicated in the MPEP. Examiner additionally notes however that for such a declaration to be considered timely, it must be made/submitted before Final/close of prosecution (see MPEP 2155.01, 717.01(a), and e.g. 717.01(f)). Differences in the inventive entity/authorship are illustrated below (strikeout for persons common). PNG media_image1.png 642 1048 media_image1.png Greyscale Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in particular an Abstract Idea falling under the (c) mental processes grouping (concepts performable in the human mind including an observation, evaluation, judgement, opinion), not ‘integrated into a practical application’ at Prong Two of Step 2A and without ‘significantly more’ at Step 2B. Step 1: The claim(s) in question are directed to a computer implemented method for segmenting/delineating one or more objects/organs within medical imagery. (Step 1: Yes) . Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. Representative claim 1 explicitly recites, at a high level of generality for each instance, e.g.: (1) “determining a portion of imaging data comprising a target body region” ; (2) “determining… data indicating one or more objects” in said region; (3) “determining… data indicating a bounding area” ; (4) “modifying… the data indicating one or more objects” ; and (5) “determining… data indicating a delineation” based at least in part on modified data. Each of (1)-(5) is/are recited at a level of generality that fails to preclude an interpretation drawing them under the mental processes Abstract Idea grouping. A mentally/visually performed organ segmentation in CT/PET imagery, as could be performed by a human/clinician, reads for each instance, and even that ‘modifying’ may be drawn to a user performed manipulation of previously determined/drawn/delineated data/information indicating one or more objects/organs/anatomical regions (see MPEP 2106.04(a)(2) subsection C. A Claim that Require a Computer May Still Recite a Mental Process ). While claim 1 further requires that determining of (2) is performed by means of “automatic anatomic recognition” (based on human input (claim 5)), and dependent claims require steps (1), (3) and (4) for example performed by ‘first’, ‘second’ and ‘third’ machine learning models respectively, MPEP 2106.05(f) in addition to Examples 47-49 of the 2024 PEG, make clear that use of a machine learning model fails to exclude associated limitations from being drawn under the mental processes grouping. Reference may be made to the 2024 PEG, Example 47 claim 2, wherein using an ANN did not preclude that anomaly detection and analysis of step(s) (d) and (e) from being drawn under the mental processes grouping at Prong One. See pages 6-7 of: https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf Dependent claims are similarly analyzed at least at Prong One since they inherit this/these same limitations identified for the case of independent claim(s) 1/14/18. ( Step 2A, Prong One: Yes). Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception, distinct from the exception itself. This evaluation is performed by (1) identifying whether there are any ‘additional elements’ recited in the claim beyond the judicial exception, and (2) evaluating those ‘additional elements’ individually and in combination (weighed against the exception) to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). Examiner notes for consideration at Prong Two of 2A that MPEP 2106.05(a), (b), (c), and (e) generally concern elements that may be indicative of integration, whereas 2106.05(f), (g), and (h) generally concern elements that are not likely indicative of integration. As an additional note, ‘additional elements’ are generally limitations excluded from interpretation under the Abstract Idea groupings, and may comprise portions of limitations otherwise identified as falling under those Abstract Idea groupings of the 2019 PEG (e.g. any detection/determination/recognition that may be made mentally accompanied by the use of a neural network and/or generic computer hardware considered under the ‘apply it’ considerations of 2106.05(f)). Any ‘providing’/outputting broadly, and ‘collection’ of data (i.e. image acquisition(s)), be they images for training any learning model and/or data/images visually observable/ evaluated by a user/operator, also fail(s) to integrate at least in view of MPEP 2106.05(g) (extra-solution data gathering/output) and/or 2106.05(h) as ‘generally linking’ the exception to a field of use involving machine learning and/or imagery so acquired. Examiner also pre-emptively notes with respect to 2106.05(a), that ‘functioning of a computer’ (see fact pattern of Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016)) does not constitute operations that a general purpose computer may be programmed/configured to perform, since functioning of a computer instead concerns functions integral to the way computers operate (e.g. memory read-write for Enfish and virus scanning for Finjan). Regarding the claim(s) ‘as a whole’, the requirement for considering the claim as a whole stems from the fact that the judicial exception alone cannot provide the improvement, and any ‘additional elements’ are not evaluated in a vacuum separate from the weight of those directed to the exception (in further view of the Alice/Mayo’s roots in pre-emption). Consideration must be given to the degree/extent to which the apparent/disclosed improvement, as it is realized in recited claim language, is to the exception itself or otherwise distinct from it and captured by those limitations clearly serving as ‘additional elements’ after analysis at Prong One, in addition to how the ‘additional elements’ weigh in comparison to those limitations directed to the exception. Reference may be made to the 08/04/2025 memo affirming analysis set forth in the 2024 PEG (https://www.uspto.gov/sites/default/files/documents/memo-101-20250804.pdf) and consistent with guidance to date. The most recent SME Memo(s) are available at: https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility and more specifically: https://www.uspto.gov/sites/default/files/documents/memo-desjardins.pdf For the case of Desjardins, the claim(s) explicitly recited a limitation not drawn under/subsumed by the identified exception at Prong One, and realizing an improvement to the technical field of machine learning (serving for integration accordingly in view of 2106.05(a) – reciting an improvement to the way machine learning models are trained). The ARP’s decision in Desjardins also did not disturb the Board’s Prong One finding. The instant claims are unlike Desjardins however (do not concern any improvement to the technical field of training machine learning models), and read much more akin to an instance of ‘applying’ machine learning techniques to perform a segmentation/delineation that is (as recited at a high level of generality) otherwise and perhaps even conventionally/traditionally so, performed visually/mentally (e.g. by a clinician/medical professional evaluating CT imagery). Even if segmenting/delineating an anatomical object from such imagery is in itself useful/ practical – the utility of the exception itself does not serve for integration into a ‘practical application’ (see MPEP 2106.04(d)). Additional elements that include providing various output embodiments (claim 13), fail to serve for integration in view of MPEP 2106.05(g), and no additional elements outside of those directed to the exception itself, appear to explicitly/ specifically capture/recite any disclosed improvement in any technology and/or technical field (MPEP 2106.05(a)). With reference to MPEP 2106.05(a): It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) Even when viewed in combination, the ‘additional elements’ present do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: No; Revised Step 2A: Yes [Wingdings font/0xE0] Step 2B) . Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to ‘significantly more’ than the recited exception, i.e., whether any ‘additional element’, or combination of additional elements, adds an inventive concept to the claim. The considerations of Step 2A Prong 2 and Step 2B overlap, but differ in that 2B also requires considering whether the claims feature any “specific limitation(s) other than what is well-understood, routine, conventional activity in the field” (WURC) (MPEP 2106.05(d)). Such a limitation if specifically recited however, must still be excluded from interpretation under any of the Abstract Idea groupings. Step 2B further requires a re-evaluation of any additional elements drawn to extra-solution activity in Step 2A (e.g. gathering video/image(s)) – however no limitations appear directed to any novel collection per se. For at least the case of representative claim 1, both the receiving and final outputting are generically recited, if not WURC. Applicant may consider Longitude Licensing Ltd. v. Google LLC, No. 24-1202, (Fed. Cir. April 30, 2025) (available at https://www.cafc.uscourts.gov/opinions-orders/24-1202.OPINION.4-30-2025_2506816.pdf) (see e.g. pages 7-9). While it is the MPEP that governs Examination and not necessarily case law (2019 marking a shift away from analysis attempting to identify analogous case law from a large and growing body of possibly pertinent case law examples), this opinion and those referenced therein (e.g. Recentive in particular - precedential) Recentive Analytics, Inc., v. Fox Corp., Appeal No. 2023-2437, (Fed. Cir. Apr. 18, 2025) available at https://www.cafc.uscourts.gov/opinions-orders/23-2437.OPINION.4-18-2025_2500790.pdf serve to illustrate the manner in which claims that seek to apply broad classes of machine learning to a field of use , and/or claim limitations that do not explain/capture how a purported inventive concept/ improvement is actually achieved, are not likely to be determined eligible/enforceable. Even more recently, Constellation Designs LLC, v. LG Electronics, Appeal No. 2024-1822, (Fed. Cir. April 28, 2026) at page 17 with reference to O’Reilly v. Morse (1853), https://www.cafc.uscourts.gov/opinions-orders/24-1822.OPINION.4-28-2026_2683894.pdf Reference may also be made to the 2024 PEG describing that an improvement/ inventive concept (for ‘significantly more’ determination(s)) cannot be to the judicial exception itself. (Step 2B: No) . Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA 1. Claim s 1-20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Udupa et al. “Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto-contouring for radiation therapy planning” (cited by Applicant, hereinafter “Agrawal”) . As to claim 1 , Agrawal discloses a method comprising: receiving imaging data indicative of an object of interest ( receipt of image I ); determining a portion of the imaging data comprising a target body region of the object ( determining I B as performed at BRR, Fig. 1, Section 2.3 ); determining, based on automatic anatomic recognition ( Fig. 1, AAR-R, Section 2.4 ) and the portion of the imaging data ( I B ), data indicating one or more objects in the target body region ( Fig. 1 AAR-R output M A (I B ) ); determining, based on the data indicating the one or more objects and for each of the one or more objects ( Fig. 1 DL-R receives I B and M A (I B ) ), data indicating a bounding area of an object of one or more objects ( Fig. 1 bb(I B ) output from DL-R, Section 2.5 ); modifying ( Fig. 1 MM ), based on data indicating the bounding areas ( Fig. 1, MM considers both bb(I B ) and M A (I B ) ), the data indicating one or more objects in the target body region ( Fig. 1 MM to M A (I B ) so as to produce M M (I B ), Section 2.6 ); determining ( Fig. 1 DL-D, Section 2.7 ), based on the modified data indicating one or more objects in the target body region ( Fig. 1 DL-D considers M A (I B ) ), data indicating a delineation of each of the one or more objects ( Fig. 1 Segmented objects output from DL-D, S(I B ) ); and causing output of the data indicating the delineation of each of the one or more objects ( section 2.1 “to delineate each object O and to output a binary image S O (I B ) and the set of delineations S(I B ) for all objects” , see also Table A.1 and “image display”, Figs. 5 and 6 ). PNG media_image2.png 398 1250 media_image2.png Greyscale As to claim 2 , Agrawal discloses the method of claim 1. Agrawal further discloses the method wherein determining the portion of the imaging data comprising the target body region of the object is based on a first machine learning model ( Fig. 1 BRR ) trained to trim imaging data to an axial superior boundary and an axial inferior boundary of an indicated target body region ( page 7122 Section 2.1 “The first module BRR is a DL-network that performs BRR. 91 Given an image I, it trims I to the axial superior and inferior boundary as per the definition of body region B in the cranio-caudal direction and outputs a trimmed image I B ” , Section 2.3, etc.; while not required for the rejection of claim 2 see also US 20190259159 A1 at [0200] ). As to claim 3 , Agrawal discloses the method of claim 1. Agrawal further discloses the method wherein data indicating one or more objects in the target body region ( Fig. 1 AAR-R output M A (I B ) ) comprises a fuzzy object model mask indicating recognition an object of the one or more objects ( Section 2.4 “The second module AAR-R (Figure 1) takes the auto-trimmed image I B as inputs. It recognizes all objects in Ơ by making use of the AAR model that has been created for B and outputs a fuzzy mask FM O (I B ) fitted to I B for each object O. AAR is a general approach based on fuzzy anatomy modeling” , Section 2.4 “Exemplar recognition results for AAR-R are shown in the next section for both body regions where the slice of the fuzzy mask FM O (I B ) is overlaid on the slice of I B for several challenging objects” , etc.; while not required for the rejection of claim 3 see also US 20190259159 A1 at [0129] ). As to claim 4 , Agrawal discloses the method of claim 1. Agrawal further discloses the method wherein determining the data indicating one or more objects in the target body region comprises following a hierarchical objection recognition process based on a fuzzy object model for the target body region, wherein the fuzzy object model indicates a hierarchical arrangement of objects in the target body region ( Section 2.4 “In the model building step, a fuzzy anatomy model FAM(B) of body region B is developed as a quintuple 30 FAM(B) = (H, M, ρ, λ, η). The first element H in FAM(B) denotes a hierarchical arrangement of the objects in B. This arrangement is key to capturing and encoding detailed information about geographic layout of the objects. M is a set of fuzzy models, with one fuzzy model FM(O) for each object O in B. FM(O) represents a fuzzy mask indicating voxel-wise fuzziness … The third element ρ represents the parent-to- child relationship of the objects in the hierarchy, estimated over the training set population” ). As to claim 5 , Agrawal discloses the method of claim 1. Agrawal further discloses the method wherein automatic anatomic recognition uses a model determined based on human input without the use of a machine learning model trained for anatomic recognition ( Section 1.1 “In this paper, these complementary traits of natural intelligence (NI) of human experts versus artificial intelligence (AI) of computers embedded in algorithms constitute the central thread. Around this thread, we synergistically weave prior high-level knowledge coming from human experts with the unmatched capabilities of deep learning (DL) algorithms to meticulously harness and utilize low-level details. The resulting hybrid-intelligence (HI) methodology presented in this paper overcomes crucial gaps that currently exist in state-of -the-art DL algorithms for medical image segmentation” , see also AAR (with reference to [30] therein “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images” ) that is NI (and subsequently enriched by AI), as distinguished from e.g. deep learning (DL) algorithms DL-R and DL-D ). As to claim 6 , Agrawal discloses the method of claim 1. Agrawal further discloses the method wherein the data indicating a boundary area of an object ( Fig. 1 bb(I B ) output of DL-R, Section 2.5 ) comprises a set of stacks of two-dimensional boundary boxes having at least one boundary box for each slice of image data comprising the object ( page 7119 “DL-based recognition (DL-R), which refines the coarse recognition results of AAR-R and outputs a stack of 2D bounding boxes (BBs) for each object;” , Section 2.1 “finds 2D BBs bounding each object O slice-wise in I B and outputs a stack bb O (I B ) of 2D BBs for each object O” , Section 2.5, Fig. 2, etc., ). As to claim 7 , Agrawal discloses the method of claim 1. Agrawal further discloses the method wherein determining ( via DL-R ) the data indicating the bounding area of an object of the one or more objects ( bb(I B ) ) comprises inputting to a second machine learning model ( DL-R, Section 2.5 “The third module DL-R uses the trimmed image I B and the set of fuzzy model masks M A (I B ) = {FM O (I B ):O ∈ Ơ} output by AAR-R to determine the set of stacks of 2D BBs” ) the portion of the imaging data comprising the target body region of the object ( I B as illustrated in Fig. 1 fed via that bottom path to DL-R ) and the data indicating one or more objects in the target body region ( M A (I B ) via that lateral/left path into DL-R ). As to claim 8 , Agrawal discloses the method of claim 7. Agrawal further discloses the method wherein the second machine learning model comprises a plurality of neural networks each trained for a different area of the object ( Fig. 7 see DL-R differences in training/model building for Neck as compared to Thorax, Fig. 8, neck and thoracic areas, see also disclosure identifying DL-D as target object specific in Section 2.7, etc., ). As to claim 9 , Agrawal discloses the method of claim 1. Agrawal further discloses the method wherein modifying the data indicating one or more objects in the target body region ( modifying at Fig. 1 MM ) comprises modifying a fuzzy object model mask representing at least one of the one or more objects based on a comparison of the fuzzy object model mask to a bounding area ( page 7124 Section 2.6 “The idea of combining via MM the information present in FM O (I B ) output by AAR-R and DL-R’s output bb O (I B ) is to merge the best evidence from the two sources to create the modified fuzzy model FM O,M (I B ) and the set of all models M M (I B ) = {FM O,M (IB):O ∈ Ơ}. This morphed model is expected to “agree” with DL-R output bb O (I B ).In this stage, AI helps enrich NI by improving the anatomy model found by AAR-R” ). As to claim 10 , Agrawal discloses the method of claim 1. Agrawal further discloses the method wherein modifying the data indicating one or more objects in the target body region comprises fitting a curve to geometric centers of a plurality of bounding areas from a plurality of image slices and adjusting a fuzzy object model mask based on the curve ( page 7124 Section 2.6 “a smooth curve is fit (see Figure 4) using a second-degree polynomial as a function of the z-coordinates of the points by minimizing the mean-squared error. Subsequently, the fuzzy mask in each slice of FM O,M (I B ) is then shifted so that the center of the fuzzy object in the slice is moved to the new center on the smoothed line. Further details of the morphing process can be found in Appendix A.3” ; Fig. 4 of reference matching/corresponding to Applicant’s Fig. 4 ). As to claim 11 , Agrawal discloses the method of claim 1. Agrawal further discloses the method wherein the data indicating the delineation of each of the one or more objects ( organs O delineated by DL-D, Section 2.7 ) comprises indications of locations within the imaging data of boundaries of each of the one or more objects within one or more slices of the imaging data ( Figs. 5 and 6, see colored delineations and associated labels e.g. RBMc, eOC, LBMc, OHPh, etc., see also Tables 3-5 featuring exemplary objects e.g. left parotid gland, heart, etc., ). As to claim 12 , Agrawal discloses the method of claim 1. Agrawal further discloses the method wherein the determining the data indicating the delineation of each of the one or more objects comprising inputting the modified data indicating one or more objects in the target body region to a third machine learning model trained to delineate objects ( Fig. 1 DL-D, Section 2.7 “The model employs a typical residual encoder–decoder type of CNN but with some enhancements” ). As to claim 13 , Agrawal discloses the method of claim 1. Agrawal further discloses the method wherein causing output of the data indicating a delineation of each of the one or more objects comprises one or more of sending the data indicating the delineation of each of the one or more objects via a network, causing display of the data indicating the delineation of each of the one or more objects, causing the data indicating the delineation of each of the one or more objects to be input to an application, or causing storage of the data indicating the delineation of each of the one or more objects ( Figs. 5 and 6, in view of that causing display recited in the alternative, in further view of the manner in which each of the recited post-processing output embodiments, namely displaying, storing, and/or sending/transmission for one or more of displaying and/or storing, by means of an associated application or more broadly, all constitute known data output embodiments (see MPEP 2143 Rationale A (while no obviousness grounds need be applied, Examiner asserts further modification to Agrawal in such a capacity would be obvious to POSITA)) – see also MPEP 2106.05(g) regarding extra-solution data outputting ). As to claim 14 , this claim is the device claim corresponding to the method of claim 1 and is rejected accordingly. Regarding that generic computer structure/recited processor and memory combination, see Agrawal at page 7132 Computational Considerations “Program execution times are estimated on a Dell computer with the following specifications: 6-core Intel i7-8700 3.2-GHz CPU with 32-GB RAM,RTX 2080ti with 11-GB RAM, and running the Linux Ubuntu operating system” , etc.. As to claim 15 , this claim is the device claim corresponding to the method of claim 2 and is rejected accordingly. As to claim 16 , this claim is the device claim corresponding to the method of claim 5 and is rejected accordingly. As to claim 17 , this claim is the device claim corresponding to the method of claim 7 and is rejected accordingly. As to claim 18 , this claim is the system claim corresponding to the method of claim 1 and is rejected accordingly, comprising a computing device ( see Agrawal as applied above for the case of claim 14 ) in addition to an ‘imaging device’ interpreted under a plain meaning and configured to generate imaging data ( see Agrawal disclosure regarding input image I, PET/CT scan images/data, and implied/inherently required imaging device used for sourcing such images – e.g. page 7119 “The HI system was tested on 26 organs in neck and thorax body regions on CT images obtained prospectively from 464 patients in a study involving four RT centers” - see also MPEP 2106.05(h) regarding ‘additional elements’ (since an ‘imaging device’ cannot itself be drawn under the mental processes Abstract Idea grouping) that fail to serve for integration and instead generally link to a field-of-use; see also 2106.05(b) and II. therein since a processor/memory combination implements the steps of the method and claim 18 does not concern any Particular Machine as defined therein – see also therein, claims ineligible under Alice/Mayo, even if eligible under machine-or-transformation test, are ineligible ). As to claim 19 , this claim is the system claim corresponding to the method of claim 2 and is rejected accordingly. As to claim 20 , this claim is the system claim corresponding to the method of claim 5 and is rejected accordingly. Additional References Prior art made of record and not relied upon that is considered pertinent to applicant's disclosure: Additionally cited references (see attached PTO-892) otherwise not relied upon above have been made of record at least in view of the manner in which they evidence the general state of the art. While e.g. MPEP § 2120 recommends against providing redundant, unnecessary and/or duplicative grounds of rejection (see sub-section I Choice of Prior Art; Best Available – “merely cumulative” rejections should be avoided), Examiner notes that Udupa et al. (US 2019/0259159 A1) appears applicable in alternative prior-art based grounds of rejection, in view of at least that disclosure identified above for claims 2-3 ([0200], [0129] “The object hierarchy H depicted in FIG. 9 was chosen for constructing FAM(B, G), where 3D renderings for different parts (object models) are illustrated. Fuzzy object model building will follow the hierarchy H by starting from root object of BTSkn, and then to other offspring objects. Recognition in the following section 2.2 will also follow the same hierarchy” , etc.,). Inquiry Any inquiry concerning this communication or earlier communications from the examiner should be directed to IAN L LEMIEUX whose telephone number is (571)270-5796. The examiner can normally be reached Mon - Fri 9:00 - 6:00 EST. 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 on 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. /IAN L LEMIEUX/Primary Examiner, Art Unit 2669 Application/Control Number: 18/771,929 Page 2 Art Unit: 2669 Application/Control Number: 18/771,929 Page 3 Art Unit: 2669 Application/Control Number: 18/771,929 Page 4 Art Unit: 2669 Application/Control Number: 18/771,929 Page 5 Art Unit: 2669 Application/Control Number: 18/771,929 Page 6 Art Unit: 2669 Application/Control Number: 18/771,929 Page 7 Art Unit: 2669 Application/Control Number: 18/771,929 Page 8 Art Unit: 2669 Application/Control Number: 18/771,929 Page 9 Art Unit: 2669 Application/Control Number: 18/771,929 Page 10 Art Unit: 2669 Application/Control Number: 18/771,929 Page 11 Art Unit: 2669 Application/Control Number: 18/771,929 Page 12 Art Unit: 2669 Application/Control Number: 18/771,929 Page 13 Art Unit: 2669 Application/Control Number: 18/771,929 Page 14 Art Unit: 2669 Application/Control Number: 18/771,929 Page 15 Art Unit: 2669 Application/Control Number: 18/771,929 Page 16 Art Unit: 2669
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