DETAILED ACTION
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 .
Claims 1-24 are pending in the application.
Of the above claims 10-12, 22-24 are withdraw from consideration
Examiner’s Note: The examiner has cited particular passages including column and line numbers, paragraphs as designated numerically and/or figures as designated numerically 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 claims, other passages, paragraphs and figures of any and all cited prior art references may apply as well. It is respectfully requested from the applicant, in preparing an eventual response, to fully consider the context of the passages, paragraphs and figures as taught by the prior art and/or cited by the examiner while including in such consideration the cited prior art references in their entirety as potentially teaching all or part of the claimed invention. MPEP 2141.02 VI: “PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS."
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/16/2023 was filed after the mailing date of the first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Election/Restrictions
Applicant’s election without traverse of Group I, claims 1-9 and 13-21, in the reply filed on 04/02/2026 is acknowledged.
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 1-9, 13-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: the claim recites an apparatus, which is a mechanical device. Thus, the claim is to a manufacture or a machine, which are statutory categories of invention.
Step 2A Prong one: the claim recites
a) receive design data corresponding to an object;
b) determine, based on the design data, features of the object;
c) determine, of the determined features, at least one classification for at least one determined feature; and
d) generate production data based on the design data, the determined features, and the determined at least one classification.
The limitation of receive design data, determine. based on the design data, features of the object, determine at least one classification, and generate production data based on the design data, the determined features, and the determined at least one classification, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the steps from practically being performed in the mind. The steps “receive”, “determine”, generate” in the context of this claim encompasses collecting data, analyzing the data (including identifying and classifying features), and generating output based on that analysis. Such activities constitute mental processed, which are abstract ideas. For example, a human designer or engineer could review a design, identify features, classify those features, and produce manufacturing instructions based on that classification. Therefore, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls withing the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: Besides the abstract ideas, the claim recites the additional element of a memory and at least one processor. But the memory and the processor are recited so generically, (no details of improving the functioning of the computer itself other than that is a memory and a processor) that they represent no more than mere instructions to apply the judicial exceptions on a computer (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)). The claim merely uses a computer as a tool to perform an abstract idea. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B: The claim as a whole does not amounts to significantly more than the recited
exception. The claim has two additional elements. The memory and processor, which is configured to perform limitations (a) through (d). As explained previously, the memory and processor are at best the equivalent of merely adding the words “apply it” to the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept. Thus, the additional elements do not amount to significantly more. Even when considered in combination, these additional elements represent mere instruction to apply an exception, which do not provide an inventive concept. The claim is not eligible.
Regarding claim 2, the claim depends on claim 1, recites the same abstract idea and additional element of using a learning model. The “machine learning mode” merely invokes a generic data analysis technique to perform the abstract idea identified in claim 1. Accordingly, the limitation amounts to no more than applying the abstract idea using generic computer functionality. The claim is not eligible.
Regarding claim 3, the claim depends on claim 1, recites the same abstract idea and additional element of classification that a feature is a critical feature. The additional element merely constitutes a mental categorization or labeling of data. Such classification is part of the abstract idea of analyzing and organizing information and does not impose a meaning limit on the claim. The claim is not eligible.
Regarding claim 4, the claim depends on claim 1+3, recites the same abstract idea and additional element of using a machine model for classifying critical features. The “machine learning mode” merely invokes a generic data analysis technique to perform the abstract idea identified in claim 1+3. Accordingly, the limitation amounts to no more than applying the abstract idea using generic computer functionality. The claim is not eligible.
Regarding claim 5, the claim depends on claim 1+3, recites the same abstract idea and additional element of determine of determine for at least one of the features classified as a critical feature, a tolerance threshold for dimensional accuracy of the critical feature when the object is produced. The additional element merely represents further analysis and decision-making based on classified data directed the mental processes. The claim is not eligible.
Regarding claim 6, the claim depends on claim 1+3+5, recites the same abstract idea and additional element of determined tolerance threshold is different from a tolerance threshold for another determined feature of the object when the object is produced. The additional element merely refines the abstract analysis by specifying a variation in result. It covers performance of the limitation in the mind. The claim is not eligible.
Regarding claim 7, the claim depends on claim 1, recites the same abstract idea and additional element of the production data is 3D print data. The additional element merely represents a field of use. The claim does not recite any specific technological improvement in 3D printing. Accordingly, the claim remains directed to an abstract idea and is not eligible.
Regarding claim 8, the claim depends on claim 1, recites the same abstract idea and additional element of receive, from a user, information corresponding to (i) a re-designation of a determined feature, (ii) a re-designation of a determined classification, (iii) an additional feature of the object beyond the features determined by the processor, or (iv) an additional classification for a feature beyond the at least one classification determined by the processor. The additional elements merely add data input and manipulation, which are themselves abstract activities. The claim is not eligible.
Regarding claim 9, the claim depends on claim 1, recites the same abstract idea and additional element of generating of production data includes adjusting a production parameter based on the at least one classification that a determined feature is a critical feature. The claim, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. The addition element merely represents, for an example, a human designer modifying a parameter of the design based on observation. The claim is not eligible.
Regarding claims 13-21, they are substantially similar to claims 1-9 that merely directed to the method to implement the system of claims 1-9, and do not correct the issues set forth above. The claims are likewise not eligible.
Claim Rejections - 35 USC § 102
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 –
(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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 13-14 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ojha et al. US Pub. No. 2023/0055488 (“Ojha”).
Regarding claim 1, Ojha discloses an apparatus [Fig. 1 and 5] comprising:
at least one processor [501]; and
at least one memory [503],
wherein the at least one memory stores computer-readable instructions which, when executed by the least one processor, cause the processor to:
receive design data corresponding to an object [3D model 101 of fig. 1 – see Par. 0017-0022];
[0017] Referring now to FIG. 1, a block diagram of an exemplary system 100a for extracting and classifying manufacturing features from a three-dimensional (3D) model of a product is illustrated, in accordance with some embodiments of the present disclosure. The system 100a includes a feature identification device 100. In some embodiments, the 3D model 101 of the product may be a 3D Computer Aided Design (CAD) model of the product. Further, in some other embodiments, a boundary representation (B-rep) based Computer Aided Design (CAD) model.
[0019] The graph generation module 102 may be configured to receive the 3D model 101 of the product.
determine, based on the design data, features of the object;
[0018] The feature identification device 100 may perform various operations to identify the manufacturing feature of the product. Further, to perform various operations, the feature identification device 100 may include a graph generation module 102, a score assigning module 103, a cumulative score determination module 104, a sub-graph extractor 105, a parameter extractor 106, a feature vector determination module 107, and a feature classification module 108. Additionally, the feature identification device 100 may also include a data store (not shown in FIG. 1) to store various data and intermediate results generated by the modules 102-108.
determine, of the determined features, at least one classification for at least one determined feature [see Par. 0023, 0032-0033]; and
[0023] The feature classification module 108 may be configured to determine to a type of manufacturing feature for each of the sub graphs. It should be noted that the feature classification module 108 may determine the type based on corresponding node feature vector and the edge feature vector.
generate production data based on the design data, the determined features, and the determined at least one classification [see Par. 0002, 0058].
[0002] A manufacturing feature in context of Computer Aided Manufacturing (CAM) is defined by set of topological entities, namely faces and edges within a Boundary Representation (B-Rep) based 3D model. The manufacturing feature may be a result of certain manufacturing process like casting, forming, material removal, and the like, being performed to achieve a reference topological shape. Further, the Computer Aided Design (CAD) models employ design features such as, extrude, revolve, and Boolean operations, in order to create geometrical shapes. Therefore, additional processing is required for extracting higher-level features, i.e., manufacturing features. Typically, extraction of the higher-level manufacturing features from the CAD models is done algorithmically and commonly by a Feature Recognition (FR) technique. The FR technique automates the flow from CAD to CAM, therefore integration of both is essential building block of Computer Integrated Manufacturing (CIM) systems. Further, the FR technique also have an application in Manufacturability evaluation and cost assessment.
Regarding claim 2, Ojha discloses the features of the object are determined using a machine learning model [GNN – par. 0005, 0023, 0032-0033].
Regarding claim 13-14, they are directed to the method of steps to implement the system as set forth in claims 1-2. Therefore, they are rejected on the same basis as set forth hereinabove.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 3-9, 15-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ojha as applied to claim 1 above, and further in view of Jaiswal et al. US Pub. No. 2020/0098195 (“Jaiswal”).
Regarding claim 3, Ojha teaches at least one classification includes a classification that a feature is a pocket, slot, hold, etc. [0032 - it should be noted that using a Graph Neural Network (GNN) model of a feature classification module (same as the GNN model 108a of the feature classification module 108) may be utilized for determination of the type of manufacturing feature. In some embodiments, a confidence score may be assigned to each of the sub-graphs corresponding to the type of manufacturing feature. The model is trained on a predefined set of features. (for example, pocket, slot, hole, etc.) which are represented as graphs. This trained model is then deployed in the final system to predict the manufacturing feature type.]. Ojha does not expressly teach the feature is a critical feature.
Jaiswal teaches systems and methods may support identification and redesign of segments in a 3D model that are below 3D printer resolution. Specifically, Jaiswal teaches at least one classification includes a classification that a feature is critical feature.
[0004] In one example, a method may be performed, executed, or otherwise carried out by a design system, such as a CAE system. The method may include accessing a 3D model representative of an object to be constructed by a 3D printer; extracting a cross-sectional slice of the 3D model perpendicular to a build direction for construction of the object by the 3D printer; and identifying a critical thin segment in the cross-sectional slice with a segment size smaller than a printer resolution of the 3D printer. Identifying the critical thin segment may include segmenting the cross-sectional slice into printable segments and non-printable segments and using machine learning model trained using geometrical features computed on thin regions to classify each of the non-printable segments as critical or non-critical. The method may further include modifying the 3D model by thickening the critical thin segment such that the segment size of the critical thin segment satisfies a thickening criterion with respect to the printer resolution and providing the modified 3D model to the 3D printer for construction of the object.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the system of Ojha with a classification that a feature is critical feature of Jaiswal. The motivation for doing so would has been to prevent failed builds and structure weaknesses due to the limitation of 3D printer resolution relative to complex digital designs. Standard CAD systems may produce models with region too fine for a physical printer to replicate accurately.
Regarding claim 4, Ojha teaches features are determined to be classified as critical features using machine learning model [par. 0004].
Regarding claim 5, Jaiswal teaches determine, for at least one of the features classified as a critical feature, a tolerance threshold for dimensional accuracy of the critical feature when the object is produced [see par. 0024, 0039, 0049, 0074, 0078].
Regarding claim 6, Jaiswal teaches the determined tolerance threshold is different from a tolerance threshold for another determined feature of the object when the object is produced [par. 0068 - the thin segment redesign engine 110 may extend each redesigned slice symmetrically by half of the layer height (e.g., thickness); 0074 - The tolerable overhang angle may be a characteristic specific to the 3D printer used to construct the object (e.g., as supplied by a printer manufacturer) or a general limitation parameter applied to designs. As an illustrative example, if a 3D printing process tolerates a 10° overhang angle, then the thin segment redesign engine 110 may set the slant angle of the inverted conical thickening shape 710 as equal to 10° or less than 10°; see further par. 0077, 0082, 0088].
Regarding claim 7, Ojha in view of Jaiswal teaches design data is design data corresponding to an object to be 3D printed, and wherein the production data is 3D print data [see fig. 8 of Jaiswal].
Regarding claim 8, Jaiswal teaches receive, from a user, information corresponding to (i) a re-designation of a determined feature, (ii) a re-designation of a determined classification, (iii) an additional feature of the object beyond the features determined by the processor, or (iv) an additional classification for a feature beyond the at least one classification determined by the processor [see par. 0022, 0040, 0049]
Regarding claim 9, Jaiswal teaches generating of production data includes adjusting a production parameter based on the at least one classification that a determined feature is a critical feature [see steps 812, 814, 816, 818, 820 of fig. 8].
Regarding claims 15-21, they are directed to the method of steps to implement the system as set forth in claims 3-9. Therefore, they are rejected on the same basis as set forth hereinabove.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. No. 2019/0101905 to Zhang et al. teach a method of converting an image of a manufactured circuit to a plurality of representative contours, the plurality of representative contours corresponding to printed features in the manufactured circuit. Specifically, Zhang et al. teach the circuit analysis program 174 can automatically determine whether the feature width and/or separation distances comply with critical dimension (CD) values (e.g., are above a minimum value and below a maximum value) required by specification 100 to determine the manufacturability of a product.
US Pub. No. 2021/0042455 to Ulu et al. teach a method for analyzing and correcting a model for manufacturability by identifying the important features and determining the modifications to the design that affects the shape minimally.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT HUY TRAN whose telephone number is (571)272-7210. The examiner can normally be reached M-F 7:00-4:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini S Shah can be reached at 571-272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VINCENT H TRAN
Primary Examiner
Art Unit 2115
/VINCENT H TRAN/Primary Examiner, Art Unit 2115
Prosecution Insights