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 .
DETAILED ACTION
This is a Non-Final Office Action of the instant application 18/012,651
(hereinafter the ‘651 application) filed on 12/22/2022. The ‘651 application claims priority under 35 U.S.C. § 371 to PCT US2020/046085 filed 8/13/2020, as well as to India application 202031026481 (6/23/2020). A certified copy of each has been received and placed on the record.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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, 13-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rothstein et al., U.S. Publication No. 2008/0174599, hereinafter Rothstein and Liu et al., U.S. Patent No. 10,762,699, hereinafter Liu.
With regard to claims 1, 8, and 15, which teach “A computer-implemented method for generating a geometric component in a computer-aided design (CAD) environment, the computer-implemented method comprising: determining, by a data processing system, a geometric operation to be performed on at least one geometric component in the CAD environment based on a CAD command selected by a user;” Rothstein teaches a system, method, and program product for generating a component in a computer aided design environment, where a user initiates the request (see paragraphs 2, 11, and 37).
With regard to claims 1, 8, and 15, which further teach “determining one or more candidate groups, each of the one or more candidate groups comprising one or more candidates in the geometric component suitable for performing the geometric operation using one or more trained machine learning models; identifying at least one candidate group on which the geometric operation is to be performed from the one or more candidate groups;” Rothstein teaches analyzing relationship between faces that imply that a feature could be added (has a high likelihood) (see paragraphs 37-40). Here groups of faces are evaluated where they meet at a common edge and are identified as a potential area to add a graphical feature.
With regard to claims 1, 8, and 15, which further teach “and performing the geometric operation on the respective one or more candidates in the identified at least one candidate group”, Rothstein teaches enabling user selection of a desired geometric result to be added to the geometric model as an explicitly defined feature (see paragraphs 41-46).
Rothstein teaches the automatic recognition of features of a 3D model by analyzing spatial or geometric relationship between other features (see paragraphs 2-4, 29, and 38), but doesn’t specifically describe the method used for identification. Liu teaches a system for identifying features of a 3D CAD model, similar to that of Rothstein, but further specifically describes using machine learning for feature identification (see column 1, lines 32-55 and column 2, lines 30-58). It would be obvious to one of ordinary skill in the art at the time of the invention to utilize the machine learning described in Liu in the feature identification system of Rothstein to better use learned methods and training date to properly identify 3D model features.
With regard to claim 2, 9, and 16, which further teach “wherein determining the one or more candidate groups comprises: generating feature data associated with the geometric component, wherein the feature data comprises object feature data associated with the geometric component; predicting a plurality of candidates in the geometric component suitable for performing the geometric operation based on the generated feature data using the one or more trained machine learning models; and creating the one or more candidate group from the plurality of the candidates based on pre-defined grouping rules”, Rothstein teaches analyzing relationship between faces that imply that a feature could be added (has a high likelihood) (see paragraphs 37-40). This generates a set of implied features that a plurality of candidate groups could form (at their intersection). Here groups of faces are evaluated where they meet at a common edge and are identified as a potential area to add a graphical feature. Where “pre-defined” grouping rules may be utilized to determining groups.
With regard to claim 6, which further teaches “further comprising: displaying the geometric component indicating the one or more candidates in the identified at least one candidate group on a graphical user interface”, Rothstein teaches displaying the geometric component indicating the candidates identified as a candidate group on the GUI (see paragraphs 32-34 and Figures 1-5).
With regard to claim 7, 14, and 20, which further teach “further comprising: identifying the one or more trained machine learning models from a plurality of trained machine learning models based on the geometric operation to be performed on the geometric component”, Liu teaches using one of various machine learning models to evaluate connection features between elements in a CAD design (supra).
Claims 3-5, 10-12, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rothstein et al., U.S. Publication No. 2008/0174599, hereinafter Rothstein and Liu et al., U.S. Patent No. 10,762,699, hereinafter Liu, as noted above, in further view of Gadh et al., U.S. Patent No. 6,629,065, hereinafter Gadh.
With regard to claims 3, 10, and 17, which further teach “wherein predicting the plurality of candidates in the geometric component suitable for performing the geometric operation comprises: computing a probability of the geometric operation likely to be performed on each of the objects in the geometric component using the one or more trained machine learning models; and identifying the plurality of candidates suitable for performing the geometric operation based on the probability of the geometric operation likely to be performed on each of the objects in the geometric component”;
While Rothstein and Liu identify intersections between faces that need be evaluated for connection blending when within a predefined threshold distance (supra), they don’t go into the specifics of organizing them based upon probability of intersection (and associated need for connection blending). Gadh teaches a computer aided design system where a user generates a CAD model that evaluates intersections of multiple elements (see column 9, line 36 through column 10, line 21), similar to that of Rothstein and Liu but further determines the probability of intersection and orders the them accordingly for further operations (see column 11, line 49 through column 12, line 40). It would be obvious to one of ordinary skill in the art at the time of the invention to utilized a prioritization of connections in order to not trouble the user or the system with connections less likely to need attention.
With regard to claims 4, 11, and 18, which further teach “wherein creating the one or more candidate groups from the plurality of the candidates based on the pre-defined grouping rules comprises: computing a probability value for each of the one or more candidate groups based on the probability value associated with the candidates in the respective candidate group”;
< see the above rejection to claim 3, which is equally applicable here >
With regard to claims 5, 12, and 19, which further teach “wherein identifying the at least one candidate group from the one or more candidate groups comprises: sorting the one or more candidate groups comprising the one or more candidates based on the computed probability values for the one or more candidate groups, respectively”;
< see the above rejection to claim 3, which is equally applicable here >
Conclusion
The prior are made of record and not relied upon is considered pertinent to applicant’s disclosure: Mezghanni et al. (US 2023/00149234), Baran et al. (US 2020/0218835), Mehr et al. (US 2020/0211276), Sanchez Bermudez et al. (US 2020/0210845), and Yu et al. (US 2010/0305906).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS G BONSHOCK whose telephone number is (571)272-4047. The examiner can normally be reached M-F 7:15 - 4:45.
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/DENNIS G BONSHOCK/Primary Examiner, Art Unit 3992