DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-24 are presented for examination.
Claim Rejections - 35 USC § 101
3. 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.
3.1 Claims 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Is the claim directed to a statutory category?
Yes. The claims are to a method (claim 1), a non-transitory medium (claim 9), a system (claim 17) for time- and resource-efficient processing of multi-dimensional models of products.
Step 2A- Prong One
The claim(s) recite(s) a method (claim 1), a medium (claim 9), a system (claim 17), comprising: The step of: “converting a product representation stored in a computer-readable file to a mesh representation, the product representation comprising a multi-dimensional model of an object”; “generating a graph representation from the mesh representation, the graph representation comprising a set of vertices, each vertex associated with a set of coordinates in multi-dimensional space”; and “processing the compound vector representation through a ML system to generate a prediction associated with the object”, under the broadest reasonable interpretation fall under a mathematical concept or otherwise a mental process. Therefore, the claims are directed to an abstract idea, by use of generic computer components and thus are clearly directed to an abstract idea, as constructed.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional limitation such as: “a non-transitory … medium”, “one or more processors”, “instructions”, “machine learning (ML) systems”, and “a computing device”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0028], [0059]-[0062], and fig.1) which can be of any type, including general-purpose computer (para [0062]) previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; the step of: “providing a compound vector representation as a data structure comprising a set of vectors, each vector in the set of vectors comprising an m-bit vector that encodes a respective vertex of the set of vertices, the m-bit vector comprising a set of bit groups, each bit group representing a respective coordinate associated in the set of coordinates of the respective vertex”, under the broadest reasonable interpretation, reasonable fall under data gathering and processing activities that are pre-solution activities” are also well-known, routine and conventional activities and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as previously discussed above with reference to the integration of abstract idea into a practical application, the additional elements of: “a non-transitory … medium”, “one or more processors”, “instructions”, “machine learning (ML) systems”, and “a computing device”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0028], [0059]-[0062], and fig.1) which can be of any type, including general-purpose computer (para [0062]) previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; the step of: ““providing a compound vector representation as a data structure comprising a set of vectors, each vector in the set of vectors comprising an m-bit vector that encodes a respective vertex of the set of vertices, the m-bit vector comprising a set of bit groups, each bit group representing a respective coordinate associated in the set of coordinates of the respective vertex”, under the broadest reasonable interpretation, reasonable fall under data gathering and processing activities that are pre-solution activities” are also well-known, routine and conventional activities and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101. Therefore, using computer components amount to no more than mere instructions to perform the abstract, and thus are not sufficient to amount to significantly more than the recited abstract, as constructed.
3.2 Dependent claims 2-8, 10-16, 18-24 merely include limitations pertaining to further mathematical computations (claim 2, 10, 18), “wherein providing a compound vector representation as a data structure comprises, for each vertex: normalizing each coordinate to a normalization range to provide normalized coordinates; binning and discretizing normalized coordinates to adjusted values; and mapping adjusted values to indices of a respective vector” (mathematical concept). (claim 3, 11, 19); “wherein normalizing is based on a maximum value of a coordinate within sets of coordinates across all vertices in the set of vertices” (mathematical concept); (claim 4, 12, 20); “wherein each bit group has a single bit set equal to a first value and all other bits set equal to a second value” (mathematical concept or otherwise mental process); (claim 5, 13, 21); “wherein the prediction comprises classifying the object to at least one category in a set of categories” (mental process); (claim 6, 14, 22); “wherein the prediction comprises predicting a similarity between the object and at least one other object” (mental process or otherwise a mathematical concept); (claim 7, 15, 23) “wherein the product representation comprises a boundary representation (BRep)” (mental process or otherwise a mathematical concept); (claim 8, 16, 24) “wherein the mesh representation comprises a triangular mesh representation” (mental process or otherwise a mathematical concept); all of which further amount to further mathematical concept and/or mental process similar to that already recited by the independent claims and already addressed above and thus are further not patent eligible under 35 USC 101.
Claim Rejections - 35 USC § 103
4. 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.
4.0 Claim(s) 1, 5-9, 13-17, and 21-24 are rejected under 35 U.S.C. 103 as being unpatentable over Schmitter et al. (USPG_PUB No. 2019/0251218), in view of Lambourne (USPG_PUB No. 2022/0156430).
4.1 In considering claims 1, 9, 17, Schmitter et al. teaches a computer-implemented method for time- and resource-efficient processing of multi-dimensional models of products through machine learning (ML) systems, the method comprising:
converting a product representation stored in a computer-readable file to a mesh representation (see para [0034], mesh conversion procedure that needs to be carried out to convert a NURBS CAD model into a mesh, which includes triangles or quads, or tetrahedra and hexahedra, or other types of meshes. [0067] Then, in a step S230, the solution surface SSF can be converted into a mesh that serves as manufacturing-ready data for a real object, [0084] By using data processing device 20, a mathematical formulation is established which performs a lossless data transformation to convert the digital cardinal B-spline representation of the IGA or FEA solution into an H-spline based representation. [0098], In this case, once adaptive refinement is completed the solution surface SSF is converted into a mesh that will be used as input into manufacturing devices), the product representation comprising a multi-dimensional model of an object (see fig.2)); however, he does not specifically show generating a graph representation from the mesh representation, the graph representation comprising a set of vertices, each vertex associated with a set of coordinates in multi-dimensional space; providing a compound vector representation as a data structure comprising a set of vectors, each vector in the set of vectors comprising an m-bit vector that encodes a respective vertex of the set of vertices, the m-bit vector comprising a set of bit groups, each bit group representing a respective coordinate associated in the set of coordinates of the respective vertex; and processing the compound vector representation through a ML system to generate a prediction associated with the object. Lambourne teaches the step of generating a graph representation from the mesh representation, the graph representation comprising a set of vertices, each vertex associated with a set of coordinates in multi-dimensional space (see para [0008], Finally, the present systems and techniques can avoid the loss of information about relative topological locations of nearby entities, as results when using approaches to B-Rep segmentation that translate the B-Rep data structure into a face adjacency graph., [0057] Various approaches can be employed to provide the network (for the machine learning algorithm) with the concise information from the B-Rep data structure. In general, the collections of faces or edges are represented using vectors of numbers for faces, edges and coedges in a multi-dimensional embedding space. In addition, using the feature matrices in one or more convolution layers of a CNN of the machine learning algorithm can involve identifying similarity between a current collections of faces and/or edges and previously seen collections of faces and/or edges.); providing a compound vector representation as a data structure comprising a set of vectors, each vector in the set of vectors comprising an m-bit vector that encodes a respective vertex of the set of vertices (see para [0025], Convolutional kernels of the neural network architecture can be defined with respect to oriented coedges in the data structure. In the neighborhood of each coedge (also referred to as a halfedge or an oriented edge) a small collection of faces, edges and coedges can be identified and patterns in the feature vectors from these entities detected by specific learnable parameters. The feature vectors are arrays of numbers generated from information about the geometry of each face and edge extracted from the solid in B-Rep format. 0052] For example, for face features, the network (for the machine learning algorithm) can be given a one-hot vector encoding of the possible surface types (e.g., plane, cylinder, cone, sphere, torus, B-spline, and NURBS), and one additional value can be used to indicate a rational NURBS surface; the m-bit vector comprising a set of bit groups, each bit group representing a respective coordinate associated in the set of coordinates of the respective vertex (0052] For example, for face features, the network (for the machine learning algorithm) can be given a one-hot vector encoding of the possible surface types (e.g., plane, cylinder, cone, sphere, torus, B-spline, and NURBS), [0057] Various approaches can be employed to provide the network (for the machine learning algorithm) with the concise information from the B-Rep data structure. In general, the collections of faces or edges are represented using vectors of numbers for faces, edges and co-edges in a multi-dimensional embedding space.); and processing the compound vector representation through a ML system to generate a prediction associated with the object (see para [0057] Various approaches can be employed to provide the network (for the machine learning algorithm) with the concise information from the B-Rep data structure. In general, the collections of faces or edges are represented using vectors of numbers for faces, edges and coedges in a multi-dimensional embedding space. In addition, using the feature matrices in one or more convolution layers of a CNN of the machine learning algorithm can involve identifying similarity between a current collections of faces and/or edges and previously seen collections of faces and/or edges. Various measures of similarity can be used, e.g., in accordance with a particular multi-dimensional embedding space being used. For example, the identifying 270 can use similarity of the vectors of numbers assessed using cosine similarity or Euclidean distance).
Schmitter et al. and Lambourne are analogous art because they are from the same field of endeavor and that the model analyzes by Lambourne is similar to that of Schmitter et al. Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Lambourne with that of Schmitter et al. because Lambourne teaches the improvement of performance (see para [0007]).
4.2 Regarding claims 5, 13, 21, the combined teachings of Schmitter et al. and Lambourne teaches that wherein the prediction comprises classifying the object to at least one category in a set of categories (see Lambourne para [0060], For face classification tasks, a final convolution unit 420 generates only matrix which includes the per-face segmentation scores for each of the u classes. [0065] For face classifications, the per-face segmentation scores for each class u.sub.i can be calculated as follows. Moreover, to classify faces, information can be averaged from their surrounding edges.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Lambourne with that of Schmitter et al. because Lambourne teaches the improvement of performance (see para [0007]).
4.3 With regards to claims 6, 14, and 22, the combined teachings of Schmitter et al. and Lambourne teaches that wherein the prediction comprises predicting a similarity between the object and at least one other object (see Lambourne para [0057], In addition, using the feature matrices in one or more convolution layers of a CNN of the machine learning algorithm can involve identifying similarity between a current collections of faces and/or edges and previously seen collections of faces and/or edges. [0066], In addition, as noted above, using 220 the feature matrices in the at least one convolution layer of the convolutional neural network of the machine learning algorithm (to recognize the at least one collection of faces or edges) can include identifying 270 the at least one collection of faces or edges as being similar to at least one of multiple other collections of faces or edges in other 3D models processed by the machine learning algorithm. Various measures of similarity can be used, e.g., in accordance with a particular multi-dimensional embedding space being used. For example, the identifying 270 can use similarity of the vectors of numbers assessed using cosine similarity or Euclidean distance.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Lambourne with that of Schmitter et al. because Lambourne teaches the improvement of performance (see para [0007]).
4.4 As per claims 7, 15, 23, the combined teachings of Schmitter et al. and Lambourne teaches that wherein the product representation comprises a boundary representation (BRep) (see Lambourne para [0056], n general, the geometry information about the trimmed parametric surfaces can include boundary representation flag information indicating an orientation of a face, an edge, a coedge, a surface, or a curve.-[0089], Thus, the 3D modeling program(s) 904 can be CAD program(s) 116 and can provide an artificial neural network architecture that operates directly on boundary representation models using convolutional kernels defined relative to coedges of the boundary representation models.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Lambourne with that of Schmitter et al. because Lambourne teaches the improvement of performance (see para [0007]).
4.5 Regarding claims 8, 16, 24, the combined teachings of Schmitter et al. and Lambourne teaches that wherein the mesh representation comprises a triangular mesh representation (see Lambourne para [0008], urther, by working directly on the original B-Rep topology, the present systems and techniques can avoid the requirement (imposed when using a triangle mesh representation of an object) to generate high quality meshes. Further see Schmitter [0012] To perform FEA on a CAD model, which is typically represented using NURBS, the representation of the geometry needs to be converted into a mesh, which includes triangles or quadrilaterals to represent surfaces or hexahedra and tetrahedral to represent volumes. This incompatibility is at the origin of a time-consuming and cumbersome mesh conversion procedure that needs to be carried out to convert a NURBS CAD model into a mesh, which includes triangles or quads, or tetrahedra and hexahedra, or other types of meshes.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Lambourne with that of Schmitter et al. because Lambourne teaches the improvement of performance (see para [0007]).
5.0 Claim(s) 2-4, 10-12, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Schmitter et al. (USPG_PUB No. 2019/0251218), in view of Lambourne (USPG_PUB No. 2022/0156430), in further view of Thomas (USPG_PUB No. 2019/0130058).
5.1 As per claims 2, 10, 18, the combined teachings of Schmitter et al. and Lambourne teaches most of the instant claims; however, he does not expressly teach that wherein providing a compound vector representation as a data structure comprises, for each vertex: normalizing each coordinate to a normalization range to provide normalized coordinates; binning and discretizing normalized coordinates to adjusted values; and mapping adjusted values to indices of a respective vector. Thomas teaches that wherein providing a compound vector representation as a data structure comprises, for each vertex: normalizing each coordinate to a normalization range to provide normalized coordinates (see para [0205] The method also includes normalizing 2530 the determined basis functions such that, for each index in the mesh, the sum of all nonzero coefficients sharing that index for all basis functions over the mesh is equal to one. [0215] 6.7.5 Normalization of the Basis [0216] As described previously, each U-spline basis function is defined by a function coefficient vector b.sub.ϕ.sub.A. For simplicity ϕ.sub.A is dropped in favor of A. These vectors represent the coefficients of a non-normalized basis for the U-spline space. To produce a basis that forms a partition of unity, the coefficient vectors must be normalized. The normalized basis is obtained by solving the linear system [00033] The normalized vectors of coefficients are given by v.sub.Ab.sub.A. Because any basis for a U-spline space can be normalized, all coefficient vectors presented hereafter are assumed to be normalized. [0217] Theorem 1. Any Basis for a U-Spline Space can be Normalized to Form a Partition of Unity.); binning and discretizing normalized coordinates to adjusted values (see para [0103] The Bernstein indices on an element form a discrete finite-dimensional space. It is useful to define the difference vector: [0213], Given a function index support set ϕ, the smoothness constraint matrix S.sub.ϕ is formed by removing all columns from the global smoothness matrix S that correspond to indices i.Math.ϕ. Also, any rows consisting entirely of zeros after this removal step are also removed.); and mapping adjusted values to indices of a respective vector (see para [0006], owever, each geometry is a mapped rectangle. Since nearly every shape of interest in the real world has a non-rectangular topology, enhancements to NURBS. [0103] The Bernstein indices on an element form a discrete finite-dimensional space. It is useful to define the difference vector: [0213], Given a function index support set ϕ, the smoothness constraint matrix S.sub.ϕ is formed by removing all columns from the global smoothness matrix S that correspond to indices i.Math.ϕ. Also, any rows consisting entirely of zeros after this removal step are also removed.).
Schmitter et al., Lambourne, and Thomas are analogous art because they are from the same field of endeavor and that the model analyzes by Thomas is similar to that of Schmitter et al. and Lambourne. Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Thomas with that of Schmitter et al. and Lambourne because Thomas teaches the improvement of quality and flexibility of shape representation, the accuracy, robustness, and efficiency of simulation (see para [0065]).
5.2 Regarding claims 3, 11, and 19, the combined teachings of Schmitter et al., Lambourne, and Thomas teaches that wherein normalizing is based on a maximum value of a coordinate within sets of coordinates across all vertices in the set of vertices (see Thomas para [0203] 3. The functions determined in the previous step must be normalized so that for each index in the mesh, the sum of all nonzero coefficients sharing that index for all basis functions over the mesh is equal to one. 0205] The method also includes normalizing 2530 the determined basis functions such that, for each index in the mesh, the sum of all nonzero coefficients sharing that index for all basis functions over the mesh is equal to one. Finally, the determined (and normalized) set of functions are output for further use in CAD, FEA, or other uses.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Thomas with that of Schmitter et al. and Lambourne because Thomas teaches the improvement of quality and flexibility of shape representation, the accuracy, robustness, and efficiency of simulation (see para [0065]).
5.3 As per claims 4, 12, 20, the combined teachings of Schmitter et al., Lambourne, and Thomas teaches that wherein each bit group has a single bit set equal to a first value and all other bits set equal to a second value (see Thomas para [0203] 3. The functions determined in the previous step must be normalized so that for each index in the mesh, the sum of all nonzero coefficients sharing that index for all basis functions over the mesh is equal to one. 0205] The method also includes normalizing 2530 the determined basis functions such that, for each index in the mesh, the sum of all nonzero coefficients sharing that index for all basis functions over the mesh is equal to one. Finally, the determined (and normalized) set of functions are output for further use in CAD, FEA, or other uses.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Thomas with that of Schmitter et al. and Lambourne because Thomas teaches the improvement of quality and flexibility of shape representation, the accuracy, robustness, and efficiency of simulation (see para [0065]).
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
6.1 Scott (USPG_PUB No. 2022/0067241) teaches a Flex Representation in Computer Aided Design (CAD) and Computer Aided Engineering (CAE).
6.2 Krishnaswamy et al. (USPG_PUB No. 2022/0215145) teaches a machine learning for rapid automatic computer-aided engineering modeling.
7. Claims 1-24 are rejected and this action is non-final. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE PIERRE-LOUIS whose telephone number is (571)272-8636. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
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, EMERSON C PUENTE can be reached at 571-272-3652. 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.
/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 September 3, 2026
Prosecution Insights