DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/25/2026 has been entered.
This Office Action is in response to the claims filed on 03/25/2026.
Claims 1, 6-8, 13-15, and 19-20 have been amended.
Claims 1-4, 6-11, 13-17, and 19-20 are pending.
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 .
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the
references as applied to the claims below for the convenience of the applicant. Although
the specified citations are representative of the teachings in the art and are applied to
the specific limitations within the individual claim, other passages and figures may apply
as well. Examiner may also include cited interpretations encompassed within parenthesis, e.g. (Examiner’s interpretation), for clarity. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/28/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Claim Objections:
Acknowledgement is made of amended claim 15 to correct “step for” language.
Objection to claim 15 is withdrawn.
Claim Rejections under 35 U.S.C. § 112(a):
Acknowledgement is made of amended claims 6, 13, and 19 to correct written description rejections.
Rejections to claims 6, 13, and 19 are withdrawn.
Claim Rejections under 35 U.S.C. § 101:
Acknowledgement is made of amended independent claims 1, 8, and 15. Applicant’ arguments have been fully considered but are not persuasive. Rejections to claims 1-4, 6-11, 13-17, and 19-20 are maintained.
Applicant argues [Pg.9] that under Step 2A Prong 1 of the Alice/Mayo test, the amended claims are not directed to an abstract idea since “[t]he computing hardware and media recited in the claims cannot be manipulated by the human mind”, “a normalizing procedure that improves the performance of the computing device”, amended limitations “are directed to the efficiency of the computing platform”, “[m]annually or mentally manipulating physical computing hardware and manually or mentally transforming complex graphical constructs is practically impossible and does not involve human-like observations, evaluations, judgments, or opinions. Further, one cannot use the mind to improve a computer display.”, amended claims “integrate the abstract idea into a practical application under Step 2A, Prong 2”, and the claims “amount to significantly more than the judicial exception”. Applicant’s arguments have been fully considered, but the Examiner respectfully disagrees.
Under Step 2A Prong 1 of the Alice/Mayo test, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2106.04(a)(2). As can be seen in Claim Rejections – 35 U.S.C. §101 section below, given the broadest reasonable interpretation in light of the Specification, the independent claims are directed towards Mathematical Concepts and/or Mental Processes per MPEP 2106.04(a)(2)(I)/(III). It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." Additionally, the courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. Regarding Applicant’s “improves the performance of the computing device” argument - as can be seen in Claim Rejections – 35 U.S.C. §101 section below, per MPEP 2106.05(f)(2) “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mathematical concepts) does not integrate a judicial exception into a practical application or provide significantly more [ ] Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept.” Thus, Applicant’s arguments not persuasive.
Claim Rejections under 35 U.S.C. § 103:
Acknowledgement is made of amended claims. Applicant’ arguments have been fully considered but are not persuasive. Rejections to claims 1-4, 6-11, 13-17, and 19-20 are maintained.
Applicant argues art of reference from Office Action dated 1/12/2026 does not teach or suggest amended claim limitations – specifically “a display that provides selection …”, “improving computational performance”, “normalization”, and “limiting the values of procedural parameters to a specified range”. After careful evaluation, the Examiner respectfully disagrees. As shown in Claim Rejections – 35 U.S.C. §103 section below, referenced art from Office Action dated 1/12/2026, in combination, does disclose amended limitations. Per Applicants “improving computational performance” argument – Sminchisescu [P.0056] discloses “[b]y training and utilizing machine-learned models for implicit object representation, the computational costs associated with resizing and/or scaling explicit representations (e.g., computation cycles, memory, processing resources, power, etc.) can be significantly reduced.” Thus, Applicant’ arguments not persuasive.
Claim Rejections - 35 USC § 101
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.
Claims 1-4, 6-11, 13-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception (an abstract idea), as it has not been integrated into a practical application and the claim(s) further do/does not recite significantly more than the judicial exception. Examiner has evaluated the claim(s) under the framework provided in MPEP 2106 and has provided such analysis below.
To determine if a claim is directed to patent ineligible subject matter, the Court
has guided the Office to apply the Alice/Mayo test, which requires:
Step 1. Determining if the claim falls within a statutory category of a Process, Machine, Manufacture, or a Composition of Matter (see MPEP 2106.03);
Step 2A. Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea (MPEP 2106.04);
Step 2A is a two-prong inquiry. MPEP 2106.04(II)(A).
Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2106.04(a)(2).
The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 2B. If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106).
Step 1:
Claims 1-4, 6-7 are directed to a method, as such these claims fall within the statutory category of a process.
Claims 8-11, 13-14 are directed to a system, as such these claims fall within the statutory category of machine.
Claims 15-17, 19-20 are directed to a computer readable medium, as such these claims fall within the statutory category of manufacture.
Step 2A, Prong 1:
The examiner submits that the foregoing claim limitations constitute abstract ideas, as the claims are directed towards Mental Processes performed on a computer and/or Mathematical Concepts, given the broadest reasonable interpretation.
In order to apply Step 2A, a recitation of claims is copied below. The limitations of those claims which describe an abstract idea are bolded.
As per claim 1, the claim recites the limitations of:
transforming the reference representation into a common representation by sampling points associated with the reference representation of the physical object; (As drafted and under its broadest reasonable interpretation, in light of the Specification, this limitation amounts to Mathematical Concepts per MPEP 2106.04(a)(2)(I). Mathematical Concepts are defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. Per Applicant’s Spec. disclosure [P.0023] “The common representation is a set of 3D points for which a value of signed distance is assigned based on the reference representation. The common representation is thus a sampling of the SDF (i.e. signed distance function) for the reference representation.” Per MPEP 2106.04(a)(2)(I)(A), “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols [ ] Examples of mathematical relationships recited in a claim include: iv. organizing information and manipulating information through mathematical correlations”. Also, per MPEP 2106.04(a)(2)(I)(C), “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping [ ] Examples of mathematical calculations recited in a claim include: i. performing a resampled statistical analysis to generate a resampled distribution”. Thus, this limitation is directed towards Mathematical Concepts.)
optimizing, using a processor, the plurality of candidate procedural models
by comparing a procedural value and a reference value of a differentiable signed distance function for each of the points associated with the reference representation of the physical object while normalizing procedural parameters to a specified range to provide a selected procedural model (As drafted and under its broadest reasonable interpretation, this limitation is directed towards Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)). Mental Processes are defined as concepts that can practically be performed in the human mind (e.g. observations, evaluations, judgments, opinions), or by a human using pen and paper as a physical aid. For instance, the human mind can reasonably compare (i.e. evaluate, judge) procedural/reference values of a distance function (i.e. mathematical concept) for each of the associated points, with/without the aid of pen and paper. And Mathematical Concepts are defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. The utilization of a distance function to derive variables to be compared amounts to Mathematical Concepts. The amended limitation of normalizing procedural parameters to a specified range also amounts to Mathematical Concepts.)
Step 2A, Prong 2:
As per claim 1, this judicial exception is not integrated into a practical application because the additional claim limitations outside the abstract idea only present Mere Instructions To Apply An Exception and/or Insignificant Extra Solution Activity. In particular, the claim recites the additional limitations:
accessing, from a memory component, a reference representation of a physical object; (The additional element amounts to Insignificant Extra-Solution Activity (mere data gathering) per MPEP 2106.05(g) and/or Mere Instructions to Apply an Exception per MPEP 2106.05(f). Per MPEP 2106.05(f)(2), “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mathematical concepts) does not integrate a judicial exception into a practical application or provide significantly more [ ] examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, v. Requiring the use of software to tailor information and provide it to the user on a generic computer”.)
accessing, from the memory component, a plurality of candidate procedural models corresponding to the reference representation based on the common representation, each candidate procedural model including semantic, adjustable editing parameters corresponding to the physical object; (The additional element amounts to Insignificant Extra-Solution Activity (mere data gathering) per MPEP 2106.05(g) and/or Mere Instructions to Apply an Exception per MPEP 2106.05(f). Per MPEP 2106.05(f)(2), “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mathematical concepts) does not integrate a judicial exception into a practical application or provide significantly more [ ] examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, v. Requiring the use of software to tailor information and provide it to the user on a generic computer”.)
producing, using the processor, a 3D, editable procedural model of a reconstructed shape using the reference representation of the physical object and the selected procedural model, the 3D editable procedural model defining an image to be rendered in two dimensions (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, this limitation invokes computers merely as a tool to perform an existing process. The existing process being producing a “procedural model”, which Applicant has defined Spec. [P.0018] as “a collection of efficient algorithms that can generate a specific 3D representation of an object”. Relevant examples the courts have found to be mere instructions to apply an exception because they do no more than merely invoke computers as a tool (see MPEP 2106.05(f)(2)(v)) is i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, v. Requiring the use of software to tailor information and provide it to the user on a generic computer.)
rendering the image in the two dimensions on a display device (The additional element amounts to Insignificant Extra-Solution Activity (mere data outputting) per MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity.)
while providing an editing interface configured for selection of at least the semantic, adjustable editing parameters or traditional 3D modeling parameters. (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, this limitation invokes computers merely as a tool to perform an existing process. The existing process being adjusting/editing image/model parameters, which can reasonably be performed by hand (i.e. redrawing an object based on differing parameters.))
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B:
For step 2B of the analysis, the Examiner must consider whether each claim limitation individually or as an ordered combination amounts to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The additional elements as described in Step 2A Prong 2 are not sufficient to amount to significantly more than the judicial exception because the additional limitations are considered directed towards Mere Instructions To Apply An Exception and/or Insignificant Extra Solution Activity.
Per MPEP 2106.05(g), “the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional”. Per MPEP 2106.05(d)(II), “[t]he courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory, v. Electronically scanning or extracting data from a physical document”.
Per MPEP 2106.05(f), “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mathematical concepts) does not integrate a judicial exception into a practical application or provide significantly more
For the foregoing reasons, claim 1 is directed to an abstract idea without significantly more and is rejected as not patent eligible under 35 U.S.C. 101.
Independent claim 8 recites substantially the same subject matter as claim 1 and is rejected under similar rationale and further failure to add significantly more.
Independent claim 15 recites substantially the same subject matter as claim 1 and is rejected under similar rationale. Claim 15 further recites A non-transitory computer-readable medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations. The additional features amount to Field of Use and Technological Environment per MPEP 2106.05(h). “[L]imitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception include: [ ] iv. Specifying that the abstract idea of [mathematical concepts and/or mental processes] that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer”. Therefore, claim 15 is directed to an abstract idea without significantly more and is rejected as not patent eligible under 35 U.S.C. 101.
Dependent claims 2 and 9 further recite accessing a set of initial parameters for a procedural graph for each available procedural model; (The additional element further amounts to Insignificant Extra-Solution Activity (mere data gathering) and/or Mere Instructions to Apply an Exception per MPEP 2106.05(f)/(g).)
generating a 3D, reconstructed shape for each set of initial parameters; (The additional element amounts to Mere Instructions to Apply an Exception (MPEP 2106.05(f)). For instance, this limitation is directed towards mere instructions to implement an abstract idea or other exception on a computer.)
and storing the procedural graph for the 3D, reconstructed shape closest to the common representation. (The additional element amounts to Insignificant Extra-Solution Activity (mere data gathering, post-solution activity) per MPEP 2106.05(g) and/or Mere Instructions to Apply an Exception per MPEP 2106.05(f).)
Therefore, the claims are rejected as not patent eligible under 35 U.S.C. §101.
Dependent claims 3, 10 and 16 further recite determining the 3D, reconstructed shape closest to the reference representation by minimizing a mean-squared error of signed distances between locations on the 3D, reconstructed shape and the reference representation. The additional element further amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)). Therefore, the claims are rejected as not patent eligible under 35 U.S.C. §101.
Dependent claims 4, 11, and 17 further recite determining the 3D, reconstructed shape closest to the reference representation by minimizing a value of a loss function. The additional element further amounts to Mental Processes per MPEP 2106.04(a)(2)(III)) and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)). Therefore, the claims are rejected as not patent eligible under 35 U.S.C. §101.
Dependent claims 6, 13, and 19 further recite scanning the physical object to capture partial, LiDAR image data; (The additional element amounts to Insignificant Extra-Solution Activity (mere data gathering, selecting a particular data source or type of data to be manipulated) per MPEP 2106.05(g) and/or Field of Use and Technological Environment (requiring that the abstract idea for modeling shapes be limited to data captured by LiDAR) per MPEP 2106.05(h))
and using the partial, LiDAR image data to sample the points associated with the reference representation (The additional element amounts to Insignificant Extra-Solution Activity (selecting a particular data source or type of data to be manipulated, insignificant application) per MPEP 2106.05(g) and/or Field of Use and Technological Environment (requiring that the abstract idea for modeling shapes be limited to data captured by LiDAR) per MPEP 2106.05(h)).
Therefore, the claims are rejected as not patent eligible under 35 U.S.C. §101.
Dependent claims 7, 14, and 20 recite wherein at least one of the candidate procedural models includes portions of the physical object not present in the reference representation. The additional element elaborates on the candidate procedural models, thus further amounting to Insignificant Extra-Solution Activity and/or Mere Instructions to Apply an Exception per MPEP 2106.05(f)/(g). Therefore, the claims are rejected as not patent eligible under 35 U.S.C. §101.
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.
Claims 1-2, 6-9, 13-15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Storti et al. US Pub. No. 20120287129 A1 (hereinafter referred to as “Storti”) in view of Taguchi et al. US Patent No. 9208609 B2 (hereinafter referred to as “Taguchi”), in further view of Sminchisescu et al. US Pub. No. 20240161470 A1 (hereinafter referred to as “Sminchisescu”), and in further view of Mazzanti US Pub. No. 2008/0036761 A1 (hereinafter referred to as “Mazzanti”). Hereinafter, the combined art referred to as “STSM”.
Regarding claim 1, Storti discloses, accessing, from a memory component, a reference representation of a physical object; (“obtaining volumetric image data (i.e. reference representation of an object) of a three-dimensional region containing a first object [ ] the volumetric data comprises voxel-based data of an anatomical image, for example, image data obtained from CT, MRI, or PET scanning” [P.0012-0013]. Examiner interprets “volumetric image data” to mean a “reference representation of an object” due to Applicant’s disclosure “The reference representation can be obtained, as an example, by scanning a physical object” Spec. [P.0017]. The data is interpreted as from memory because “ the first set of signed distance values or the functional representation of the first object, are stored on computer-readable media.” [P.0012])
transforming the reference representation into a common representation by sampling points associated with the reference representation of the physical object; (“a 3D example is shown in FIGS. 3A-3D of images of a wavelet f-rep model (i.e. common representation) of a talus (a bone in the foot). The bone was volumetrically scanned to obtain a 128x128x128 grid of intensity values (i.e. reference representation). A level set method was employed to produce a 128x128.x128 grid of SDF values [ ] The grid of SDF values together with a wavelet specification [ ] comprises the wavelet f-rep model of the talus, so a solid model is obtained” [P.0085] . Examiner interprets the wavelet f-rep model (i.e. common representation) to have been transformed by “sampling points associated with the reference representation of the object” because “So to recap what we have seen so far: (1) f-reps (or implicit functions) can be used to represent geometric objects. SDFs are very well-behaved f-reps that can be fully synthesized from sampled values [ ] Wavelet f-reps, therefore, allow us to employ sampled values of the SDF as a modeling representation” [P.0077-0081])
,
producing, using the processor (“A new approach is disclosed for computer (includes the processor) representation/modeling of geometric shapes” [P.0041]), a 3D, editable procedural model of a reconstructed shape using the reference representation of the physical object and the selected procedural model. (“modeling operations including visualization, Boolean operations, skeletal editing, and computation of internal and surface properties can all be performed” [P.0116]. Also, “a 3D example is shown in FIGS. 3A-3D of images of a wavelet f-rep model” [P.0085])
Storti fails to specifically disclose accessing, from a memory component, a plurality of candidate procedural models corresponding to the reference representation based on the common representation, each candidate procedural model including semantic, adjustable editing parameters corresponding to the physical object; optimizing, using a processor, the plurality of candidate procedural models by comparing a procedural value and a reference value of a differentiable signed distance function for each of the points associated with the reference representation of the physical object while normalizing procedural parameters to a specified range to provide a selected procedural model, the 3D editable procedural model defining an image to be rendered in two dimensions; and rendering the image in the two dimensions on a display device while providing an editing interface configured for selection of at least the semantic, adjustable editing parameters or traditional 3D modeling parameters.
However, Taguchi discloses accessing, from the memory component (“The method can be performed in a processor 150 connected to memory and input/output interfaces as known in the art.” Taguchi [Col.2 Ln.16]), a plurality of candidate procedural models corresponding to the reference representation based on the common representation; (“The 3D point cloud (i.e. reference representation. See Specification disclosure P.0031) is converted 105 to a distance field 102 (i.e. common representation. See Specification disclosure P.0023). The distance field is used in a RANSAC-based primitive shape fitting process 110, where a set of two or more candidate shapes 100 (i.e. procedural models. See Specification disclosure P.0018) are hypothesized 111 by using a minimal number of points required to determine parameters of a corresponding shape.” Taguchi [Col.2 Ln.1-6])
optimizing, using the processor (“The method can be performed in a processor 150 connected to memory and input/output interfaces as known in the art.” Taguchi [Col.2 Ln.16]), the plurality of candidate procedural models by comparing a procedural value and a reference value of a differentiable signed distance function for each of the points associated with the reference representation of the physical object to provide a selected procedural model; (“A score 121 is determined for each shape candidate [ ] The method selects 120 the best candidate primitive shape (i.e. procedural model) that has a minimal score among the candidates. Optionally, the parameters of the best primitive shape can be refined 130 (i.e. optimizing) using a gradient-decent procedure. To determine a set of primitive shapes in a point cloud (i.e. reference representation), we iterate the process 110 after subtracting 140 the selected primitive shape from the distance field.” Taguchi [Col.2 Ln.7-14] Also see the following sections for further clarification - “Conversion from Point Cloud to Distance Field” [Col.2 Ln.19] and “Primitive Shape Fitting Using Distance Field” Taguchi [Col.3 Ln.27].)
Storti and Taguchi are analogous art as they relate to functional based representations of objects, specifically utilizing 3D point data. Storti discloses “A method for modeling an object [ ] includes obtaining volumetric scan data of a region and segmenting the scan data to identify a first object [ ] the first set of signed distance values provides a function-based representation of the object” [Abstract] and Taguchi discloses “the embodiments of the invention provide a method for fitting primitive shapes to 3D point clouds using distance fields. Input to the method is a 3D point cloud 101. The 3D point cloud can be obtained as a scan of a 3D sensor” [Col.1 Ln.61-66]. Therefore, it would have been obvious to one of ordinary skill in the art before the
Applicant's effective filling date of the claimed invention to have modified Storti to
incorporate the teachings of Taguchi, as disclosed above, because “[r]epresenting 3D point clouds as a set of primitive shapes is desired for compact modeling and fast processing.” Taguchi [Col.1 Ln.22-24])
Storti and Taguchi fail to specifically disclose each candidate procedural model including semantic, adjustable editing parameters corresponding to the physical object, while normalizing procedural parameters to a specified range, and the 3D editable procedural model defining an image to be rendered in two dimensions and rendering the image in the two dimensions on a display device while providing an editing interface configured for selection of at least the semantic, adjustable editing parameters or traditional 3D modeling parameters.
However, Sminchisescu discloses each candidate procedural model including semantic, adjustable editing parameters corresponding to the physical object (“The method can include determining [ ] an implicit object (i.e. physical object) representation of the object and semantic data indicative of one or more surfaces of the object [ ] The method can include adjusting, by the computing system, one or more parameters of the machine-learned implicit object representation model” Sminchisescu [P.0006]. Note: An implicit object is interpreted as a physical object because “[t]he object can be any physical object as described previously in the specification” Sminchisescu [P.0091].)
while normalizing procedural parameters to a specified range (“to avoid learning difficulties associated with implicit representation models (e.g., spectral bias, etc.), sample encoding can be utilized [ ] each sample (e.g., latent code, etc.) can be encoded using Fourier mapping [ ] where the samples can first be unposed using a root rigid transformation T0−1, and can be normalized into [0,1]3 with a shared bounding box
B
= [bmin, bmax]” Sminchisescu [P.0029]. Note: Normalized into [0,1] is interpreted as a specified range due to Applicant’s disclosure “with procedural parameters being normalized between the respective bounds so that a parameter's range becomes zero to one.” Spec. [P.0032])
Although, Sminchisescu also fails to specifically disclose the 3D editable procedural model defining an image to be rendered in two dimensions and rendering the image in the two dimensions on a display device while providing an editing interface configured for selection of at least the semantic, adjustable editing parameters or traditional 3D modelling parameters.
However, Mazzanti discloses the 3D editable procedural model defining an image to be rendered in two dimensions and rendering the image in the two dimensions on a display device while providing an editing interface configured for selection of at least the semantic, adjustable editing parameters or traditional 3D modelling parameters (“An object of the present invention is to provide a method for the editing of three-dimensional graphic models which allows the user to simply and intuitively modify an existing model. Another object of the invention is to provide a method for processing which allows editing of a 3D model by acting on two-dimensional views of said model.” Mazzanti [P.0014-15], “The method according to the invention further includes a stage of generating one or more items of additional data, representing geometrical and/or positional and/or semantic relations between the various structural elements of the three-dimensional model and their 2d views counterparts.” Mazzanti [P.0063])
Sminchisescu and Mazzanti are analogous art as both disclose 3D object models and involve computer-implemented methods for handling 3D data. Both reference semantic data and relationships between parts or segments of 3D objects. Each involves the processing of 3D representations and the updating or generation of model data in response to some input. Sminchisescu claims a computer-implemented method and system for training and using a machine-learned model to generate implicit representations of objects, particularly in 3D, using latent codes, spatial query points, and segment-based neural network models, and Mazzanti claims a method for editing 3D graphic models by modifying structural elements and their associations, with support for 2D views, feature-based models, and assembly modifications. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined shape parameter editing and 2D display capabilities, as Sminchisescu and Mazzanti disclose, with the Storti-Taguchi combination in order to allow “the user to simply and intuitively modify an existing model.” Mazzanti [P.0014].
Regarding claim 2, STSM discloses the method of claim 1, Storti further discloses, storing the procedural graph for the 3D, reconstructed shape closest to the common representation (“using wavelet analysis on the first set of signed distance values to generate a function-based representation of the object (i.e. procedural graph. See Applicant disclosure in Spec. P.0036), which is stored on computer-readable media.” [P.0016]. Examiner interprets wavelet analysis to include “the 3D, reconstructed shape closest to the common representation” because “Wavelet analysis provides [ ] Simple recursion relations that can be efficiently implemented [ ] exact fits (i.e. closest to) are provided for sampled values of functions” [P.0033-0035], “wavelet analysis was performed and small coefficients were removed to illustrate the capability for effective data compression. FIG. 3B was produced (i.e. 3D, reconstructed shape)” [P.0089], and “signed distance values is employed as the data defining a wavelet approximant / interpolant function that in turn serves as the f-rep (i.e. common representation) for the solid” [P.0043])
Storti fails to specifically disclose wherein selecting the plurality of candidate procedural models further comprises: accessing a set of initial parameters for a procedural graph for each available procedural model; generating a 3D, reconstructed shape for each set of initial parameters.
However, Taguchi further discloses wherein selecting the plurality of candidate procedural models further comprises: (see claim 1 for Taguchi disclosure) accessing a set of initial parameters for a procedural graph for each available procedural model; (“The output of the method is a set of parameters 109 that define the set of primitive shapes (i.e. procedural models).” Taguchi [Col.2 Ln.14-15]. “We optionally refine 130 the parameters of the best primitive shape [ ] to determine a Jacobian matrix (i.e. procedural graph) with respect to each parameter of the primitive shape for refining the parameters.” Taguchi [Col.5 Ln.48-56]. Examiner interprets “Jacobian matrix” to mean “procedural graph” due to Applicant’s disclosure “The graph is a mathematical instruction for producing the model in three dimensions” Specification [P.0023]) generating a 3D, reconstructed shape for each set of initial parameters; (“The output of the method is a set of parameters 109 that define the set of primitive shapes (i.e. procedural models).” Taguchi [Col.2 Ln.14-15]. Examiner interprets “primitive shapes” to be 3D shapes because “the embodiments of the invention provide a method for fitting primitive shapes to 3D point clouds” Taguchi [Col.1 Ln.62-64]).
It would have been obvious to one of ordinary skill in the art before the
Applicant's effective filling date of the claimed invention to have modified Storti’s method to incorporate the teachings of Taguchi, as disclosed above, in order to achieve “compact modeling and fast processing.” Taguchi [Col.1 Ln.22-24])
Regarding claim 6, the STSM combination discloses the method of claim 1, Storti fails to specifically disclose scanning the physical object to capture partial, LiDAR image data; and using the partial, LiDAR image data to sample the points associated with the reference representation.
However, Sminchisescu further discloses, scanning the physical object to capture partial, LiDAR image data (“ the ground truth data can be or otherwise include point cloud scanning data of the object. For example, a scanning device can be utilized (e.g., a LIDAR-type scanner, etc.) to generate a point cloud indicative of the surface(s) of an object” Sminchisescu [P.0040]); and using the partial, LiDAR image data to sample the points associated with the reference representation (“to train the machine-learned implicit object representation model, a sample point pi, defined for the object, can be transformed into the N localized point sets” Sminchisescu [P.0041]).
It would have been obvious to one of ordinary skill in the art before the
Applicant's effective filling date of the claimed invention to have modified Storti’s method to incorporate the utilization of LiDAR data, as Sminchisescu discloses, in order to achieve “[a]ccurate, three-dimensional representation of objects” Sminchisescu [P.0002].
Regarding claim 7, the STSM combination discloses the method of claim 1, Storti fails to specifically disclose wherein at least one of the candidate procedural models includes portions of the physical object not present in the reference representation.
However, Taguchi further discloses, wherein at least one of the candidate procedural models includes portions of the physical object not present in the reference representation. (“After determining the distance field by solving Equation (1) (i.e. reference representation), we truncate the distance field as
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where
C
is a threshold used for the truncation. This makes the primitive (i.e. procedural model. See claim 2 for clarification) fitting process accurate even when there are missing data (i.e. not present in the reference representation).” Taguchi [Col.2 Ln.60-65]. Examiner interprets Equation (1) to include the “reference representation” due to Applicant’s disclosure “[t]he reference representation is based on the point cloud” [P.0031]).
It would have been obvious to one of ordinary skill in the art before the
Applicant's effective filling date of the claimed invention to have modified Storti to
incorporate the teachings of Taguchi, as disclosed above, in order to achieve “compact modeling and fast processing.” Taguchi [Col.1 Ln.22-24])
Regarding claim 8, Storti discloses, A system comprising: a memory component; and a processing device coupled to the memory component, the processing device to perform operations comprising: (“A new approach is disclosed for computer representation/modeling of geometric shapes including a new method for solid modeling (i.e., creating computer-based representations of three-dimensional solids) and associated methods for the fundamental operation of solid modeling” [P.0041]. It’s understood to one of ordinary skill in the art that a “computer” comprises a memory component coupled to a processing device.)
The remaining limitations recite substantially the same subject matter as claim 1 and are rejected under similar rationale.
Claim 9, the STSM combination discloses the system of claim 8. The remaining limitations recite substantially the same subject matter as claim 2 and are rejected under similar rationale.
Claim 13, the STSM combination discloses the system of claim 8. The remaining limitations recite substantially the same subject matter as claim 6 and is rejected under similar rationale.
Claim 14, the STSM combination discloses the system of claim 8. The remaining limitations recite substantially the same subject matter as claim 7 and is rejected under similar rationale.
Regarding claim 15, Storti discloses, A non-transitory computer-readable medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations comprising (“A new approach is disclosed for computer (i.e. includes a non-transitory CRM) representation / modeling of geometric shapes including a new method for solid modeling (i.e., creating computer-based representations of three-dimensional solids) and associated methods for the fundamental operation of solid modeling” Storti [P.0041])
The remaining claim limitations recite substantially the same subject matter as claim 1 and are rejected under similar rationale.
Claim 19, the STSM combination discloses the non-transitory computer-readable medium of claim 15. The remaining limitations recite substantially the same subject matter as claim 6 and are rejected under similar rationale.
Claim 20, Storti discloses the non-transitory computer-readable medium of claim 15. The remaining limitations recite substantially the same subject matter as claim 7 and are rejected under similar rationale.
Claims 3-4, 10-11, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Storti et al. US Pub. No. 20120287129 A1 (hereinafter referred to as “Storti”), in view of Taguchi et al. US Patent No. 9208609 B2 (hereinafter referred to as “Taguchi”), in view of Sminchisescu et al. US Pub. No. 20240161470 A1 (hereinafter referred to as “Sminchisescu”), in further view of Mazzanti US Pub. No. 2008/0036761 A1 (hereinafter referred to as “Mazzanti”), and in further view of Jiang, Yue, Dantong Ji, Zhizhong Han, and Matthias Zwicker. "SDFdiff: Differentiable rendering of signed distance fields for 3d shape optimization." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1251-1261. 2020 (hereinafter referred to as “Jiang”). Hereinafter, the combined art referred to as “STSMJ”.
Regarding claim 3, the STSM combination discloses the method of claim 2, but fails to specifically disclose further comprising determining the 3D, reconstructed shape closest to the reference representation by minimizing a mean-squared error of signed distances between locations on the 3D, reconstructed shape and the reference representation.
However, Jiang, which is analogous art, discloses further comprising determining the 3D, reconstructed shape closest to the reference representation by minimizing a mean-squared error of signed distances between locations on the 3D, reconstructed shape and the reference representation. (“For simplicity we choose the L2 distance between the rendered (i.e. reconstructed) and the target (i.e. reference) images as our image-based loss, that is Limg (R(Θ), I) = ||R(Θ) − I||2 “ [Pg.1254 Sec.5.1], “where Limg is a loss function measuring the distance between the target image and the rendered image from the 3D object” [Pg.1253 P.1]. Examiner interprets the loss function to be a “mean-squared error” due to Applicant’s disclosure “The loss chosen in this example is the mean-squared error” Spec. [P.0030])
Jiang is analogous art as it relates to rendering 3D shapes. Specifically, Jiang proposes “a novel approach for image-based shape optimization using differentiable rendering of 3D shapes represented by signed distance functions (SDFs)” [Abstract].
It would have been obvious to one of ordinary skill in the art before the
Applicant's effective filling date of the claimed invention to have further modified the STSM combination to include the loss function (i.e. mean-squared error) of Jiang in order to achieve “3D shape optimization by leveraging SDFs as the geometric representation to perform differentiable rendering” Jiang [Pg.1252 Sec.3].
Regarding claim 4, the STSM combination discloses the method of claim 2, but fails to specifically disclose further comprising determining the 3D, reconstructed shape closest to the reference representation by minimizing a value of a loss function.
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However, Jiang discloses, further comprising determining the 3D, reconstructed shape closest to the reference representation by minimizing a value of a loss function. (“where Limg is a loss function measuring the distance between the target image and the rendered image from the 3D object. In practice, the loss is typically accumulated over multiple target images. Getting the desired parameters Θ* is equivalent to minimizing the loss L.” Jiang [Pg.1253 P.1])
It would have been obvious to one of ordinary skill in the art before the
Applicant's effective filling date of the claimed invention to have further modified the STSM combination to include the teachings of Jiang in order to achieve “3D shape optimization by leveraging SDFs as the geometric representation to perform differentiable rendering” Jiang [Pg.1252 Sec.3].
Claim 10, the STSM combination discloses the system of claim 9. The remaining limitations recite substantially the same subject matter as claim 3 and are rejected under similar rationale.
Claim 11, the STSM combination discloses the system of claim 9. The remaining limitations recite substantially the same subject matter as claim 4 and are rejected under similar rationale.
Claim 16, the STSM combination discloses the non-transitory computer-readable medium of claim 15. The remaining limitations recite substantially the same subject matter as claim 3 and is rejected under similar rationale.
Claim 17, the STSM combination discloses the non-transitory computer-readable medium of claim 15. The remaining limitations recite substantially the same subject matter as claim 4 and are rejected under similar rationale.
Conclusion
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/ANTHONY CHAVEZ/ Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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