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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 2026 has been entered.
Status of the Claims
The amendment received on 03 March 2026 has been acknowledged and entered.
Claims 1 and 11 have been amended.
Claims 10 and 20 have been canceled.
Claims 1-9 and 11-19 are currently pending.
Response to Amendments and Arguments
Applicant's amendments filed 03 March 2026, with respect to the objection to claim 11 have been fully considered and are persuasive. Thus, the objection to claim 11 has been withdrawn.
Applicant's arguments filed 03 March 2026 in regards to the rejection of claims 1-9 and 11-19 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues (in REMARKS, pages 2-3 of 14), that regarding Step 2A, Prong one, The Office asserts the limitations of claims 1 and 11 are directed to certain methods of organizing human activity by stating in part: “generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model, as drafted, are processes that, under their broadest reasonable interpretation, covers performance of the limitations by Managing personal behavior or relationships or interactions between people… and/or a Commercial Activity...” Office Action, p. 12. Applicant respectfully traverses this assertion… Applicant respectfully submits that the steps of “receive a parameter constraint via a user interface; regenerate the optimized part model based on the parameter constraint; and generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model; and display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface” does not fall within the enumerated sub-groupings of managing personal behavior or relationships or interactions between people include social activities, teaching, and following rules or instructions.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first, the steps of “receive a parameter constraint…”; “regenerate the optimized part model…”; “ generate a quote for manufacturing of the part…”; and “display an unmachinable qualities…” under the broadest reasonable interpretation includes methods of organizing human activities (e.g. “Managing Personal Behavior or Relationships or Interactions Between People" since the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer may fall within the "certain methods of organizing human activity" grouping. Therefore, the claims as a whole recite a method of organizing human activity (i.e. Managing personal behavior, relationships or interactions between people (receiving, regenerate, generate, and display steps) and/or a Commercial Interaction (receiving, regenerate, generate, and display steps), and therefore fall within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas.
Applicant argues (in REMARKS, pages 3-4 of 14), that First, the recitation is directed to technical manufacturing optimization, not human behavior and support can be found at least in paragraphs [0049] and [0053] of the instant Specification. For instance, paragraph [0049] teaches “qualities that make a part unmachinable many include workpiece material deteriorations, tolerances, geometric features for the part to be manufactured, surface roughness, time, cost, a set of tools, fixturing system, tool accessibility, a set-up and load time for the part 124, manufacturability datum 136, and the like. If the given quality for a part is outside a predetermined range for any of these considerations the part may be considered unmachinable. The unmachinable qualities of the part may be displayed within the quote 156 or on the user interface 140.” Paragraph [0053] teaches “the processor 108 regenerate one or more of optimized part data 144, optimized part model 152, one or more toolpaths including first toolpath and optimized toolpath 148, and/or quote 156 based on the parameter constraint.” Second, a “parameter constraint” is defined by a technical design constraint, which may be a tolerance constraint, a toolpath limitations, etc. These parameter constraints are tied to machinability, not human behavior. Third, “unmachinable qualities” is any quality of the part to be manufactured causes the part to be determined as unmachinable, which may include workpiece material deteriorations, tolerances, geometric features, surface roughness, time, cost, a set of tools, fixturing system, tool accessibility, set-up and load time for the part, and the like. They are technical engineering determinations, not human interactions.
Here, the limitations recited in amended claim 1 are directed to a specific and technical processing of 3D geometric data to automatically evaluate and optimize manufacturability and produce A 3D model for a part that is capable of being manufactured. The particular steps, including the iterative machine-learning-based correlation and selection of model variations and the automatic synthesis of optimized part data and models, cannot reasonably be construed as a method of organizing human activity. In addition the modification of 3D models cannot reasonably be construed as methods of organizing human activity. Applicant respectfully submits that the claims do not encapsulate the above identified groupings of organizing human activity.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first, the claims are directed to generating a quote for manufacturability of a part which, under the broadest reasonable interpretation includes methods of organizing human activities (e.g. “Managing Personal Behavior or Relationships or Interactions Between People" since the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer may fall within the "certain methods of organizing human activity" grouping. Further, a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). Secondly, “parameter constraint” and “unmachinable qualities” (receiving data, displaying data) does not take the claims out of the method of organizing human activity grouping. Lastly, but for the generic computer components, the particular steps, including correlation and selection of model variations are directed to a method of organizing human activity. Therefore, the Examiner maintains the claims are patent ineligible.
Applicant argues (in REMARKS, pages 4-5 of 14), that regarding a Mental process, the Office asserts on pages 11-12 of the Office Action that “generate a quote for manufacturing of the part…, under their broadest reasonable interpretation, covers performance of the limitations by … a Mental Process (concepts performed in the human mind which includes observations, evaluations, judgments, and opinions).” Applicant respectfully disagrees with the characterization… The MPEP provides an example cited in dicta of Synopsys, Inc. v. Mentor Graphics Corp., 839 F. 3D 1138, 1148 (Fed. Cir. 2016) as an example of a limitation that cannot be “practically performed in the human mind.” Id. In that cited portion of its decision, the Synopsis court reasoned that a claim does not recite a mental process where the “invention involves a several-step manipulation of data that, except in its most simplistic form, could not conceivably be performed in the human mind or with pencil and paper.” Synopsys, 839 F. 3D at 1148, citing TQP Development, LLC v. Intuit Inc., 2014 WL 651935 (E.D. Tex. Feb. 19, 2014) (emphasis added). Claim 1 as amended recites a process to “receive a parameter constraint via a user interface; regenerate the optimized part model based on the parameter constraint; and generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model; and display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface.” The process, just like in Synopsys, except in its most simplistic form, could not conceivably be performed in the human mind because regenerating the optimized part model would require recomputing geometric relationships, reapplying the parameter constraint to the algorithms involved in generating the optimized part model in the first place, updating the quote that comprises changes to the 3D model and displaying the unmachinable qualities of the part and the corrections via a user interface.
In response to Applicant’s argument, the Examiner respectfully notes that the Examiner has not changed the thrust of the rejection, and discussed in the previous Office actions that the “determine/determining” and “identify/identifying” steps recited a mental process, not the “generating” steps.
Applicant argues (in REMARKS, pages 5-6 of 14), that the process recited in amended claim 1 is not akin to a human judgement or observation as the process is computational, not mental, because it requires extensive manipulation of computer-implemented data structures and the display of the unmachinable qualities, which is a highly accurate regeneration of a digital model, cannot conceivably be executed in human mind even if a human could conceptually observe the part. Even in its most simplistic form, no human judgment/observation could achieve and execute the aforementioned limitation. In Synopsys, the Court has set a benchmark for separating the simplistic form of data manipulation from inventions involve several-step manipulation of data that could not conceivably be performed in the human mind or with pencil and paper, and “receive a parameter constraint via a user interface; regenerate the optimized part model based on the parameter constraint; and generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model; and display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface” is above that benchmark.
In response to Applicant’s argument, the Examiner respectfully notes that the Examiner has not changed the thrust of the rejection, and discussed in the previous Office actions that the “determine/determining” and “identify/identifying” steps recited a mental process, not the “receive a parameter constraint…”; “regenerate the optimized part model based on the parameter constraint”; “generate a quote for manufacturing of the part based on the optimized part model…”; and “display an unmachinable qualities…” steps. However, the “receive a parameter constraint…”; “regenerate the optimized part model based on the parameter constraint”; “generate a quote for manufacturing of the part based on the optimized part model…”; and “display an unmachinable qualities…” steps does not take the claims out of the “Method or organizing human activity” grouping.
Applicant argues (in REMARKS, pages 6-7 of 14), that further, “[t]he mental process is not without limits. Examiners are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind… The USPTO recently clarified the difference between claims that “recite” an abstract idea and claims that just “involve” an abstract idea in the August 2025 Memo by considering the published USPTO examples 39 and 47. Example 39 recites limitation “training the neural network in a first stage using the first training set” does not recite a judicial exception. The Aug. 2025 Memo, p. 3.
Similarly, claim 1 as amended recites limitation “generating optimized part data as a function of the selection, using an optimized part data machine learning model trained iteratively on correlations between the 3D model and the manufacturability of the part,” which does not recite a judicial exception. Finally, Applicant respectfully notes that Examiners must establish unpatentability under 35 U.S.C. § 101 by the “preponderance of the evidence.” The August 2025 Memo, p. 5; MPEP § 706. This means that Examiners should only make a rejection when it is “more likely than not” that a claim is ineligible under 35 U.S.C. § 101, not when “an examiner is uncertain as to the claim’s eligibility.” The August 2025 Memo, p. 5. Therefore, the amended claim recites technical operations that cannot be performed mentally and are therefore not directed to a mental process under Step 2A, Prong One.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that “using a machine learning model” to generate optimized data does not take the claims out of the “Method or organizing human activity” grouping. Therefore, the Examiner in unpersuaded by Applicant’s argument.
Applicant argues (in REMARKS, pages 7-9 of 14), that regarding Step 2A, Prong two, The Office asserts on page 12 of the Office Action that “[t]he judicial exception is not integrated into a practical application.” Applicant respectfully disagrees with the assertion… Here, amended claim 1 is directed to a specific technological environment for 3D CAD modeling, manufacturability analysis based on parameter constraints, and automated quoting systems. It imposes meaning technical limits, including but not limited to, “receive a parameter constraint via a user interface; regenerate the optimized part model based on the parameter constraint; and generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model; and display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface.” The recitation is functionally integrated into the practical application of manufacturing quote…In addition, analogously to Example 47, claim 1 as amended contains non-abstract claim elements that describe technological improvements as indicated by the specification, such as the steps that receive a three-dimensional (3D) model of a part, determine manufacturability from that geometry, identify parameters that affect manufacturability, and then automatically optimize the CAD model using a machine-learning model iteratively trained on correlations between geometry and manufacturability. Just as Example 47 (claim 3) contextualizes anomaly detection within network monitoring and then performs concrete post-detection actions (e.g., determining maliciousness and dropping packets), the present claim contextualizes detection within computer-implemented CAD models by detecting “anomalous” manufacturability-impacting parameters in the model, determining which geometric variations most improve a manufacturability datum, and then taking concrete post-detection actions by selecting and replacing those parameters with the most impactful variation, generating optimized part data and an optimized 3D model, and further generating a manufacturing quote that embodies those technical changes. Thus, like the packet-dropping step that proactively improves network intrusion detection in example 3 of claim 47, the claimed replacement of parameters and synthesis of an optimized 3D model proactively improves computer-implemented manufacturability analysis and the downstream quoting pipeline.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first, unlike Example 47, Applicant’s do not provide a technical improvement to a technical problem by increasing the overall performance of the apparatus or processor(s). Instead, the claims use generic computer components as tools to implement the abstract idea of generating a quote for manufacturing a part. Generally linking the use of the judicial exception to a particular technological environment or field of use does not integrate the judicial exception into practical application – see MPEP 2106.05(h). Further, the courts determined that "[p]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101" (Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18)); and the courts also determined that "The requirements that the machine learning model be 'iteratively trained' or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 12." Therefore, the Examiner maintains the claims are patent ineligible and do not integrate the judicial exception into practical application.
Applicant argues (in REMARKS, page 9 of 14), that consistent with the July 2024 Subject Matter Eligibility Guidelines, Applicant’s specification sets forth a technological improvement (that is, a computer-implemented pipeline that transforms raw CAD input into production-ready geometry for manufacturing) and the claim reflects that improvement by reciting the end-to-end pipeline from ingesting the 3D model, detecting manufacturability-affecting parameters (analogous to anomalies), selecting and substituting the best variation (analogous to dropping packets), generating optimized part data/models, and producing a quote tied to the transformed model. Accordingly, claim 1 as amended integrates any judicial exception into a practical application and should be found eligible under 35 U.S.C. § 101.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that the claims as currently amended appear to be directed to business solution to a business problem by use of generic computer components as tools to implement the abstract idea of generating a quote. Secondly, the claims are a different fact pattern than that of Example 47. Example 47 provides a technical solution while the instant claims use generic computer components to implement the abstract idea.
Applicant argues (in REMARKS, pages 9-10 of 14), that regarding Step 2B, although the 2B analysis by the Office is moot in light of the amendments to claim 1 and the arguments above. The office alleges that the independent claims “do not include additional elements that are sufficient to amount to significantly more than the judicial exception.” Office Action, p. 13. Specifically, the Office asserts on page 14 that the steps recited in claims 1 and 11 “amount to no more than mere instructions to apply the exception using a generic computer component.” Applicant respectfully disagrees… In the present application, claim 1 as amended recites details of how a solution to a problem is accomplished, including a particular way to generate a manufacturing quote which comprises “receive a parameter constraint via a user interface; regenerate the optimized part model based on the parameter constraint; and generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model; and display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface.” In addition, claim 1 as amended also purports to improve an existing technology of generating accurate manufacturability quotes that includes “generating optimized part data as a function of the selection, using an optimized part data machine learning model trained iteratively on correlations between the 3D model and the manufacturability of the part.” As such, Applicant submits that claim 1 as amended is allowable under 35 U.S.C. §101, at least for the reasons stated above.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that the Applicant has not include the mechanism which provides an improvement to the system, processor, or models in the claims to provide significantly more. Further, Applicant has presented an abstract-idea-based solution implemented with generic technical components (machine learning models) a conventional way (feeding input/generating output) to make price adjustments. For instance, using a machine learning model to generate price quotes based on an optimized part model is not an improvement a technical field. The claims as amended appears to provide an improvement or business solution to a business problem. Therefore, the Examiner maintains the claims are patent ineligible.
Applicant argues (in REMARKS, page 10 of 14), that Claim 11 recites, substantially, the same limitations as claim 1. Therefore, Applicant submits that the rejection to claim 11 has been overcome for the same reasons as to claim 1. Applicant respectfully requests reconsideration and withdrawal of the rejection. Claims 22-9 and 12-19 depend, directly or indirectly, on claims 1 or 11 and thus recite all of the same elements as claim 1 and claim 11. Applicant, therefore, submits that claims 2-9 and 12-19 overcome these rejections for at least the same reasons as discussed above with reference to claims 1 and 11.
In response to Applicant’s argument, the Examiner respectfully disagrees for reasons stated above regarding the rejection of claim 1.
Applicant argues (in REMARKS, page 10-11 of 14), that regarding the Double Patenting Rejection, Claims 1, 4, 11 and 14 stand rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4, 11 and 14 of U.S. Patent No. 11,966,955 B1 (hereinafter “the 955 Patent”). Office Action, p. 17. Applicant respectfully traverses as the Office does not assert that the 955 Patent discloses “receive a parameter constraint via a user interface; regenerate the optimized part model based on the parameter constraint; and generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model; and display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface” as recited in amended claim 1. Accordingly, Applicant respectfully submits that claim 1 as amended is patentably distinguishable from the 955 Patent for at least the reasons discussed above.
In response to Applicant’s arguments, the Examiner respectfully disagrees and notes that Claims 1, 4, 11, and 14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5, 11, and 14 of U.S. Patent No. 11,966,955 B1 in view of Sawyer et al. (US PG Pub. 2024/0354471 A1) and Guo (US PG Pub. 2022/0414293 A1) as discussed below.
Applicant argues (in REMARKS, page 11 of 14), that Claim 11 has been amended similarly to claim 1. As noted above, claim 1 as amended is patentably distinguishable from the 955 Patent for at least the reasons discussed above. Therefore, claim 11 as amended is patentably distinguishable from the 955 Patent for at least the reasons discussed above. Claims 4 and 14 depend directly on claims 1 and 11 respectively. As noted above, claims 1 and 11 as amended are patentably distinguishable from the 955 Patent for at least the reasons discussed above. Therefore, claims 4 and 14 as amended are patentably distinguishable from the 955 Patent for at least the reasons discussed above.
In response to Applicant’s argument, the Examiner respectfully disagrees for reasons stated above regarding the Double Patenting rejection of claim 1
Applicant's arguments filed 03 March 2026 with respect to the rejection of claims 1 5, 7-9, 11, 15, and 17-19 under U.S.C. 103 have been fully considered but they are not persuasive.
Applicant argues (in Remarks, pages 11-12 or 14) that regarding Sawyer/Guo…The Office has not asserted that Sawyer discloses “receive a parameter constraint via a user interface; regenerate the optimized part model based on the parameter constraint; and generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model; and display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface” as recited in amended claim 1. Accordingly, Applicant respectfully submits that claim 1 as amended is patentably distinguishable from Sawyer for at least the reasons discussed above. Guo does not cure the deficiencies of Sawyer. The Office has not asserted that Guo discloses “receive a parameter constraint via a user interface; regenerate the optimized part model based on the parameter constraint; and generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model; and display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface” as recited in amended claim 1. As such, Applicant respectfully submits, that claim 1 as amended is patentably distinguishable over Guo and Sawyer, alone or in combination, for at least the reasons discussed above. Claim 11 recites similar limitations to claim 1. As noted above, claim 1 is patentably distinguishable over Guo and Sawyer alone or in combination, for at least the reasons discussed above. Accordingly, Applicant respectfully submits that claim 11 as amended is patentably distinguishable over Guo and Sawyer, alone or in combination, for at least the reasons discussed above. Therefore, Applicant respectfully requests the withdrawal of these rejections. Each of claims 5, 7-9, 15, and 17-19 depend, directly or indirectly, from claims 1 and 11. As noted above, claims 1 and 11 are patentably distinguishable over Guo and Sawyer, alone or in combination, for at least the reasons discussed above. Accordingly, Applicant respectfully submits that claims 5, 7-9, 15, and 17-19 are patentably distinguishable over Guo and Sawyer, alone or in combination, for at least the reasons discussed above. Therefore, Applicant respectfully requests the withdrawal of the rejections of claims 1, 5, 7-9, 11, 15, and 17-19.
In response to Applicants argument, the Examiner respectfully disagrees and notes that Sawyer et al. discloses receive a parameter constraint via a user interface (Sawyer et al.: [0020] Computer model 108 may further include any data describing and/or relating to a computer model of the part to be manufactured. Computer model 108 may include any modeling type, such as, without limitation, a wireframe, solid model, boundary representation, design intent that drives the previously described geometry (e.g., parameters, constraints and associative references to previous geometry and parameters), model parameters, Product Manufacturing Information (PMI), and/or any combination thereof). Sawyer et al. further discloses display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface (Sawyer et al.: [0034]-[0035] With continued reference to FIG. 1, processor 104 may be configured to identify unmanufacturable qualities of the part. As used in the current disclosure, “unmanufacturable qualities” is any quality of the part to be manufactured 112 causes the part to be determined as unmanufacturable. In a non-limiting example, unmachinable qualities may include work material considerations, time, cost, the tools that are currently available, set-up and load time for the part to be manufactured, and the like). Guo et al. discloses regenerate the optimized part model based on the parameter constraint (Guo: [0042] The computing system 404 then generates an output of virtually, as-machined parts 402. Data for design optimization and performance analysis 407 is then generated from the output and fed back into the CAD/CAM model 401. Data for manufacturing improvement 406 is also generated from the output and fed into the manufacturing components 408 along with the CAD/CAM model 401. The manufacturing components 408 thus manufacture the parts represented by the CAD/CAM model 401 as generally described above). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the manufacturing estimate system of Sawyer et al. to include the regeneration of optimized parts as taught by Guo in order to get the most improved models used for the manufacturing of parts (Guo: [0044]).
Applicant argues (in Remarks, pages 12-13 of 14) that regarding Guo/Sawyer/Lovell… Each of claims 2 and 12 depend, directly or indirectly, from claims 1 and 11. As noted above, claims 1 and 11 are patentably distinguishable over Guo and Sawyer, alone or in combination, for at least the reasons discussed above. Accordingly, Applicant respectfully submits that claims 2 and 12 are patentably distinguishable over Guo and Sawyer, alone or in combination, for at least the reasons discussed above. Lovell does not cure the deficiencies of Sawyer. The Office has not asserted that Lovell discloses “receive a parameter constraint via a user interface; regenerate the optimized part model based on the parameter constraint; and generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model; and display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface” as recited in amended claim 1. Accordingly, applicant respectfully submits that claim 1 as amended, and similarly claim 11 is patentably distinguishable over Guo, Sawyer, and Lovell alone or in combination, for at least the reasons discussed above. Accordingly, applicant respectfully submits that claim 2 and 12 which depend from claims 1 and 11 are patentably distinguishable over Guo, Sawyer, and Lovell alone or in combination, for at least the reasons discussed above. Therefore, Applicant respectfully requests the withdrawal of this rejection.
In response to Applicant’s arguments, the Examiner respectfully disagrees for reasons stated above regarding the rejection of claims 1 and 11 (see # 21 above).
Applicant argues (in Remarks, page 13 of 14) that regarding Guo/Sawyer/Lovell/King … Each of claims 4 and 14 depends, directly or indirectly, from claims 1 and 11. As noted above, claims 1 and 11 are patentably distinguishable over Guo, Sawyer, and Lovell, alone or in combination, for at least the reasons discussed above. Accordingly, Applicant respectfully submits that claims 4 and 14 are patentably distinguishable over Guo, Sawyer, and Lovell, alone or in combination, for at least the reasons discussed above. King does not cure the deficiencies of Sawyer. The Office has not asserted that King discloses “receive a parameter constraint via a user interface; regenerate the optimized part model based on the parameter constraint; and generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model; and display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface” as recited in amended claim 1. Accordingly, applicant respectfully submits that claims 4 and 14 as amended are patentably distinguishable over Guo, Sawyer, Lovell, and King, alone or in combination, for at least the reasons discussed above.
In response to Applicant’s arguments, the Examiner respectfully disagrees for reasons stated above regarding the rejection of claims 1 and 11 (see # 21 above).
Applicant argues (in Remarks, pages 13-14 of 14) that regarding Guo/Sawyer/Lyu … Each of claims 6 and 16 depends, directly or indirectly, from claims 1 and 11. As noted above, claims 1 and 11 are patentably distinguishable over Guo and Sawyer, alone or in combination, for at least the reasons discussed above. Accordingly, Applicant respectfully submits that claims 6 and 16 are patentably distinguishable over Guo and Sawyer, alone or in combination, for at least the reasons discussed above. Lyu does not cure the deficiencies of Sawyer. The Office has not asserted that Lyu discloses “receive a parameter constraint via a user interface; regenerate the optimized part model based on the parameter constraint; and generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model; and display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface” as recited in amended claim 1. Accordingly, applicant respectfully submits that claims 6 and 16 as amended are patentably distinguishable over Guo, Sawyer, and Lyu, alone or in combination, for at least the reasons discussed above.
In response to Applicant’s arguments, the Examiner respectfully disagrees for reasons stated above regarding the rejection of claims 1 and 11 (see # 21 above).
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 and 11-19 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more.
Step 1
Claims 1-9 are directed to a system (i.e., a machine). Claims 11-19 are directed to a method (i.e., a process). Therefore, Claims 1-20 all fall within the one of the four statutory categories of invention.
Step 2A Prong 1
Independent claims 1 and 11 substantially recite:
receive a three dimensional (3D) model from an entity, the 3D model corresponding to a part sought to be manufactured;
determine, using the 3D model, a manufacturability of the part;
identify one or more parameters affecting the manufacturability of the part;
improve the manufacturability of the part by generating an optimized part model of the part using the 3D model as a function of the part data, wherein generating the optimized part model comprises:
correlating one or more variations of the 3D model with an improvement to a manufacturability datum;
selecting at least one variation of the one or more variations of the 3D model to replace the one or more parameters, wherein the at least one variation is most strongly correlated to improving the manufacturability of the part;
generating optimized part data as a function of the selection, using an optimized part data machine learning model trained iteratively on correlations between the 3D model and the manufacturability of the part; and
generating the optimized part model of the part as a function of the optimized part data;
receive a parameter constraint;
regenerate the optimized part model based on the parameter constraint
generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model; and
display an unmachinable qualities of the part and correction to the unmachinable qualities within the quote. The aforementioned limitations as a whole recite a method or organizing human activity. The limitations, as drafted, are processes that, under their broadest reasonable interpretation, covers performance of the limitations by Managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and/or a Commercial Activity (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations) and/or a Mental Process ” (concepts performed in the human mind which includes observations, evaluations, judgments, and opinions). That is, nothing in the claim elements preclude the steps from practically being performed by at least “Managing Personal Behavior or Relationships or Interactions Between People” (receive/receiving, determine/determining, identify/identifying, improve/improving, correlating/correlating, selecting/selecting, generating/generating, generating/generating, receive/receiving, regenerate/regenerating; generate/generating; and display/displaying) and/or Commercial Interaction (receive/receiving, determine/determining, identify/identifying, improve/improving, correlating/correlating, selecting/selecting, generating/generating, generating/generating, receive/receiving, regenerate/regenerating; generate/generating; and display/displaying) and/or Mental Process (determine/determining, identify/identifying).
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements: “apparatus,” “processor,” “memory,” “instructions,” “optimized part data machine learning model,” and “a user interface”; and claim 11 recites the additional elements: “at least a processor,” “optimized part data machine learning model” and “a user interface” to perform the “receive/receiving,” “determine/determining,” “identify/identifying,” “improve/improving,” “correlating/correlating,” “selecting/selecting,” “generating/generating,” “generating/generating,” “receive/receiving,” “regenerate/regenerating,” “generate/generating,” and “display/displaying” steps. The claimed computer components in the steps of claims 1 and 11 are recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea (i.e., “apparatus,” “processor,” “memory,” “instructions,” “optimized part data machine learning model,” and “a user interface” in claim 1 and “at least a processor,” “optimized part data machine learning model,” and “a user interface” in claim 11) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Each of the additional limitations is no more than mere instructions to apply the exception using the generic computer components (i.e., “apparatus,” “processor,” “memory,” “instructions,” “optimized part data machine learning model,” and “a user interface” in claim 1 and “at least a processor,” “optimized part data machine learning model,” and “a user interface” in claim 11). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (i.e., “apparatus,” “processor,” “memory,” “instructions,” “optimized part data machine learning model” and “a user interface” in claim 1 and “at least a processor,” “optimized part data machine learning model” and “a user interface” in claim 11). Accordingly, even in combination, 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. Thus, the claims are not patent eligible.
Step 2B
The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the (“apparatus,” “processor,” “memory,” “instructions,” “optimized part data machine learning model” and “a user interface” in claim 1 and “at least a processor,” “optimized part data machine learning model” and “a user interface” in claim 11 to perform the “receive/receiving,” “determine/determining,” “identify/identifying,” “improve/improving,” “correlating/correlating,” “selecting/selecting,” “generating/generating,” “generating/generating,” “receive/receiving,” “regenerate/regenerating,” “generate/generating,” and “display/displaying” steps amount to no more than mere instructions to apply the exception using a generic computer component. Thus, even when viewed as a whole, nothing in the claims add significantly more (i.e. inventive concept) to the abstract idea. The claims are patent ineligible.
As per dependent claim 2, the recitations, “generating the optimized part model of the part as a function of an optimized toolpath based on the optimized part data, wherein the optimized toolpath corresponds to a first tool used to manufacture the part” is further directed to a method of organizing human activity as described in claim 1.
As per dependent claim 3, the recitations, “generating a first toolpath, and wherein the first toolpath… training …” are further directed to a method of organizing human activity as described in claim 1. Further, the recitation of “a toolpath machine learning model” is another computer component recited at a high-level of generality and is merely invoked as a tool to perform the abstract idea. Similar to claim 1, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claims 4 and 14, the recitations, “generating the optimized part data…” is further directed to a method of organizing human activity as described in claims 1 and 11, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Further, the recitation of “summarize” is directed to a mental process. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 5-10 and 15-20, the limitation merely narrow the previously recited abstract idea limitations. Dependent claims 5 and 15 recites the improvement to the manufacturability datum comprises a reduction in cost. Dependent claims 6 and 16 recites the optimized part data comprises one or more altered variations of the part data. Dependent claims 7 and 17 recites the optimized part model comprises a computer aided design model. Dependent claims 8 and 18 recites the quote comprises a bill of materials for optimized part model. Dependent claims 9 and 19 recites the quote comprises portions of the optimized part model that are deemed to be unmanufacturable. For the reasons described above with respect to claims 5-10 and 15-20, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Dependent Claims 2-9 and 12-19 have been given the full two part analysis including analyzing the additional limitations both individually and in combination. Dependent Claims 2-9 and 12-19, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea of the independent claims. The dependent claims recite no additional elements that would integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Simply implementing the abstract idea on generic computer components is not a practical application of the judicial exception and does not amount to significantly more than the judicial exception. The claims are not patent eligible.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 4, 11, and 14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5, 11, and 14 of U.S. Patent No. 11,966,955 B1 in view of Sawyer et al. (US PG Pub. 2024/0354471 A1) and Guo (US PG Pub. 2022/0414293 A1). Although the conflicting claims are not identical, they are not patentably distinct from each other.
Claims 1, 5, 11, and 14 of U.S. Patent No. 11,966,955 B1 differs since it fails to recite receive a parameter constraint via a user interface; regenerate the optimized part model based on the parameter constraint; and display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface. Sawyer et al. discloses receive a parameter constraint via a user interface (Sawyer et al.: [0020] Computer model 108 may further include any data describing and/or relating to a computer model of the part to be manufactured. Computer model 108 may include any modeling type, such as, without limitation, a wireframe, solid model, boundary representation, design intent that drives the previously described geometry (e.g., parameters, constraints and associative references to previous geometry and parameters), model parameters, Product Manufacturing Information (PMI), and/or any combination thereof). Sawyer et al. further discloses display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface (Sawyer et al.: [0034]-[0035] With continued reference to FIG. 1, processor 104 may be configured to identify unmanufacturable qualities of the part. As used in the current disclosure, “unmanufacturable qualities” is any quality of the part to be manufactured 112 causes the part to be determined as unmanufacturable. In a non-limiting example, unmachinable qualities may include work material considerations, time, cost, the tools that are currently available, set-up and load time for the part to be manufactured, and the like). Guo et al. discloses regenerate the optimized part model based on the parameter constraint (Guo: [0042] The computing system 404 then generates an output of virtually, as-machined parts 402. Data for design optimization and performance analysis 407 is then generated from the output and fed back into the CAD/CAM model 401. Data for manufacturing improvement 406 is also generated from the output and fed into the manufacturing components 408 along with the CAD/CAM model 401. The manufacturing components 408 thus manufacture the parts represented by the CAD/CAM model 401 as generally described above). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify claims 1, 5, 11, and 14 of US Patent 11,966,955 B1 to include receiving parameters and displaying unmachinable qualities in the manufacturing estimate system of Sawyer et al. to identify corrections and make suggestions to improve machinability (Sawyer et al. [0035]-[0036])and further to include the regeneration of optimized parts as taught by Guo in order to get the most improved models used for the manufacturing of parts (Guo: [0044]).
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.
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.
Claims 1, 5, 7-9, 11, 15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Sawyer et al. (US PG Pub. 2024/0354471 A1) in view of Guo (US PG Pub. 2022/0414293 A1).
As per claims 1 and 11, Sawyer et al. discloses an apparatus and method for generating a manufacturing quote, the apparatus comprising:
a processor (Sawyer et al.: [0003] The apparatus includes at least a processor and a memory communicatively connected to the at least a processor), also see FIG. 1; and
a memory, wherein the memory contains instructions configuring the processor to: (Sawyer et al.: [0003] The memory containing instructions configuring the at least a processor to receive a computer mode), also see FIG. 1:
receive a three dimensional (3D) model from an entity, the 3D model corresponding to a part sought to be manufactured (Sawyer et al.: [0020] Continuing to refer to FIG. 1, processor 104 is configured to receive a computer model 108 of a part for manufacture 112… Computer model 108 may include a three-dimensional image of part for manufacture 112), also see FIG. 1;
determine, using the 3D model, a manufacturability of the part ([0023] In embodiments, Model-based definitions 116 may be visible to the user within the computer model 108 and readable by processor 104 and other manufacturing equipment. Model-based definitions 116 may be used to determine the manufacturability of the part to be manufactured 112. Model-based definitions 116 may also be used to generate a manufacturing estimate of the part to be manufactured 112);
identify one or more parameters affecting the manufacturability of the part (Sawyer et al.: [0029] With continued reference to FIG. 1, processor 104 may be configured to determine the manufacturability of the part to be manufactured 120 and as a function of a plurality of manufacturing specifications 124. As used in the current disclosure, “manufacturing specifications” are factors that affect the manufacturability of a part for manufacture. Examples of manufacturing specifications 124 may include, lead time, various expedited lead times, types of equipment needed, manufacturing operations to be performed, quantity, materials, material cost, oversized parts, undersized parts, attributes about the contact & account associated on the quote, Setup time, run time, hourly rate, quantity, part attributes, pricing options, crew size required for an operation, and the like.
improve the manufacturability of the part by generating an optimized part model of the part using the 3D model, wherein generating the optimized part model comprises (Sawyer et al.: [0035] With continued reference to FIG. 1, processor 104 may be configured to identify corrections to the part to improve machinability. Corrections to the part may include suggestions to use a different material that is more machinable):
correlating one or more variations of the 3D model with an improvement to a manufacturability datum (Sawyer et al.: [0035] Slight changes to the geometry of the features of the part may also be suggested to improve manufacturability. In embodiments, Processor 104 may be configured to output a plurality of different suggestions to improve machinability of the part for manufacture 112. In an embodiment, the corrections may be made as function of the identification of the unmanufacturable qualities of the component. For example, the processor 104 may have identified the component has a tight geometric tolerance which makes the component unmachinable. The processor 104 may identify geometric tolerance the manufacturer can offer as a function of the manufacturer specifications 124. Then the processor 104 may apply those tolerances to the component as correction to the part for manufacture 112, Part corrections may be displayed within the manufacturing quote);
selecting at least one variation of the one or more variations of the 3D model to replace the one or more parameters, wherein the at least one variation is most strongly correlated to improving the manufacturability of the part (Sawyer et al.: [0035] The processor 104 may identify geometric tolerance the manufacturer can offer as a function of the manufacturer specifications 124. Then the processor 104 may apply those tolerances to the component as correction to the part for manufacture 112, Part corrections may be displayed within the manufacturing quote).Then the processor 104 may apply those tolerances to the component as correction to the part for manufacture 112, Part corrections may be displayed within the manufacturing quote);
generating optimized part data as a function of the selection, using an optimized part data machine learning model trained iteratively on correlations between the 3D model and the manufacturability of the part (Sawyer et al.: [0036] Manufacturability training data may additionally be configured to correlate a model-based definitions 116 and manufacturing specifications 124 as an input to cost and the time to manufacture them as an output. Manufacturability training data may include model-based definitions 116, manufacturing specifications 124, examples of manufacturability of a part for manufacturer 112, examples of a manufacturability score, and the like. Examples of manufacturability of a part for manufacturer 112 and examples of a manufacturability score may include any manufacturability score or manufacturability of a part for manufacturer that was generated prior to the current prediction of manufacturability of a part for manufacturer. Manufacturability training data may be stored in a database, such as a training data database, or remote data storage device, or a user input or device. In an embodiment, a manufacturability training data may be iteratively updated with the input and output results of the manufacturability machine learning model. Updated manufacturability training data may then be used to retrain manufacturability machine learning model using a feedback loop, also see [0043]);
receive a parameter constraint via a user interface (Sawyer et al.: [0020] Computer model 108 may further include any data describing and/or relating to a computer model of the part to be manufactured. Computer model 108 may include any modeling type, such as, without limitation, a wireframe, solid model, boundary representation, design intent that drives the previously described geometry (e.g., parameters, constraints and associative references to previous geometry and parameters), model parameters, Product Manufacturing Information (PMI), and/or any combination thereof);
generate a quote for manufacturing of the part based on the optimized part model, wherein the quote comprises changes to the 3D model (Sawyer et al.: [0035] The processor 104 may identify geometric tolerance the manufacturer can offer as a function of the manufacturer specifications 124. Then the processor 104 may apply those tolerances to the component as correction to the part for manufacture 112, Part corrections may be displayed within the manufacturing quote); and
display an unmachinable qualities of the part and a correction to the unmachinable qualities within the quote via the user interface (Sawyer et al.: [0034]-[0035] With continued reference to FIG. 1, processor 104 may be configured to identify unmanufacturable qualities of the part. As used in the current disclosure, “unmanufacturable qualities” is any quality of the part to be manufactured 112 causes the part to be determined as unmanufacturable. In a non-limiting example, unmachinable qualities may include work material considerations, time, cost, the tools that are currently available, set-up and load time for the part to be manufactured, and the like… The unmachinable qualities of the part may be displayed within the manufacturing quote or on the User device.).
Sawyer et al. does not explicitly, however, Guo discloses:
generating the optimized part model of the part as a function of the optimized part data (Guo: [0042] The computing system 404 then generates an output of virtually, as-machined parts 402. Data for design optimization and performance analysis 407 is then generated from the output and fed back into the CAD/CAM model 401. Data for manufacturing improvement 406 is also generated from the output and fed into the manufacturing components 408 along with the CAD/CAM model 401. The manufacturing components 408 thus manufacture the parts represented by the CAD/CAM model 401 as generally described above); and Guo: [0031] An as-built part will be produced using virtual machining with processes that include models of a machine tool, a controller, a stock design, a cutter and tool paths. All these elements can have variations. For example, a capability of the machine will be described by an accuracy of the machine, such as axis position accuracy based on machine tool calibration data or machine specifications. The variations of the cutter will be represented by its accuracy. The variations of the stock design will be described by its accuracy or tolerance. A number of as-built parts with variations can then be produced by introducing machine cutter/stock variations into the virtual machining The virtual machining can also incorporate other process variations such as _ in tool/part deflections by including cutting force models).
regenerate the optimized part model based on the parameter constraint
(Guo: [0042] The computing system 404 then generates an output of virtually, as-machined parts 402. Data for design optimization and performance analysis 407 is then generated from the output and fed back into the CAD/CAM model 401. Data for manufacturing improvement 406 is also generated from the output and fed into the manufacturing components 408 along with the CAD/CAM model 401. The manufacturing components 408 thus manufacture the parts represented by the CAD/CAM model 401 as generally described above). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the manufacturing estimate system of Sawyer et al. to include the optimized part model as taught by Guo in order to get the most improved models used for the manufacturing of parts (Guo: [0044]).
As per claims 5 and 15, Sawyer et al. in view of Guo discloses the apparatus and method of claims 1 and 11, respectively. Sawyer et al. further discloses wherein the improvement to the manufacturability datum comprises a reduction in cost (Sawyer et al.: [0045] A manufacturing estimate 132 may additionally contain information regarding the lead time required to deliver a component of a part. A manufacturing estimate 132 may provide suggestions to make a component more affordable or more manufacturable).
As per claims 7 and 17, Sawyer et al. in view of Guo discloses the apparatus and method of claims 1 and 11, respectively. Sawyer et al. does not further disclose, however, Guo discloses wherein the optimized part model comprises a computer aided design model (Guo: [0042] The computing system 404 then generates an output of virtually, as-machined parts 402. Data for design optimization and performance analysis 407 is then generated from the output and fed back into the CAD/CAM model 401. Data for manufacturing improvement 406 is also generated from the output and fed into the manufacturing components 408 along with the CAD/CAM model 401. The manufacturing components 408 thus manufacture the parts represented by the CAD/CAM model 401 as generally described above). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the manufacturing estimate system of Sawyer et al. to include the CAD model as an optimized part model as taught by Guo in order to get the most improved models.
As per claims 8 and 18, Sawyer et al. in view of Guo discloses the apparatus and method of claims 1 and 11, respectively. Sawyer et al. further discloses wherein the quote comprises a bill of materials for optimized part model. GD&T may inform which subsets of these choices of manufacturing setups and operations are appropriate. Interpreting large quantities of GD&T in a model may be challenging either for a human combined with an algorithm or procedure. There are several standards available worldwide that describe the symbols and define the rules used in GD&T. One such standard is American Society of Mechanical Engineers (ASME) Y14.5. As used in the current disclosure, an “assembly level bill of materials” is a complete list of the components of a part to manufacture 112. An assembly level bill of materials may comprise all of the components required in an assembly.); and (Sawyer et al.: [0045] In embodiments, the manufacturing estimate 132 may be calculated as a function of setup time, hourly rate, run time, and/or quantity made. In an embodiment, processor 104 may be configured to assign an hourly rate to each of the setup time, man hours, and run time. Processor 104 may multiply the setup time, man hours, and run time by the hourly rate. That is then added to the cost of materials and other shop costs. The summation of all of these values is the manufacturing estimate 132. Processor 104 may produce manufacturing estimate 132 on a quantity curve. Thus, the manufacturing estimate 132 may fluctuate as function of the quantity of a part that is ordered. Manufacturing estimate 132 may be itemized for client convenience.
As per claims 9 and 19, Sawyer et al. in view of Guo discloses the apparatus and method of claims 1 and 11, respectively. Sawyer et al. further discloses wherein the quote comprises portions of the optimized part model that are deemed to be unmanufacturable (Sawyer et al.: [0044] Manufacturing quotes may also denote that the part is unable to be manufactured due to issues regarding manufacturing specifications 124 and model-based definitions 116. Additionally, a manufacturing quote may make suggestions on corrections to an unmachinable part in order to make it manufacturable. These suggestions may include increasing the tolerances for various features, or changing the material of the part, using other machining tools).
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Sawyer et al. (US PG Pub. 2024/0354471 A1) in view of Guo (US PG Pub. 2022/0414293 A1) and Lovell et al. (US PG Pub. 2021/0034961 A1 herein referred to as “Lovell et al. ‘961”).
As per claims 2 and 12, Sawyer et al. in view of Guo discloses the apparatus and method of claims 1 and 11, respectively. Sawyer et al. does not further disclose, however, Guo discloses:
wherein generating the optimized part model of the part as a function of the optimized part data further comprises generating the optimized part model of the part as a function of an optimized toolpath based on the optimized part data (Guo: [0031] An as-built part will be produced using virtual machining with processes that include models of a machine tool, a controller, a stock design, a cutter and tool paths. All these elements can have variations. For example, a capability of the machine will be described by an accuracy of the machine, such as axis position accuracy based on machine tool calibration data or machine specifications. The variations of the cutter will be represented by its accuracy. The variations of the stock design will be described by its accuracy or tolerance. A number of as-built parts with variations can then be produced by introducing machine/cutter/stock variations into the virtual machining The virtual machining can also incorporate other process variations such as tool/part deflections by including cutting force models). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the manufacturing estimate system of Sawyer et al. to include the optimized tool path as taught by Guo in order to command the tool motion path to machining the part (Guo: [0035]).
Sawyer et al. in view of Guo does not further disclose, however, Lovell et al. ‘961 discloses:
wherein the optimized toolpath corresponds to a first tool used to manufacture the part (Lovell et al ‘961: [0033] In some implementations, the defect removal manufacturing machine 172 is the manufacturing machine 170. Thus, the manufacturing machine 170 can be used to both build the part initially and then be used to remove any defects. There is no need to have an additional defect removal manufacturing machine 172 in such implementations, but separate machines are still possible (e.g., a first CNC milling too! 170 to build the part at one point in a manufacturing line, and a second CNC milling tool 172 to remove any defects in at a later point in the manufacturing line). Moreover, as noted above, the machine 170 can be an AM machine, and the CAM program(s) 116 can, in some implementations, control both the AM machine 170 and the SM machine 172 using toolpath specifications generated in appropriate formats for the respective machines). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the manufacturing estimate system of Sawyer et al. the manufacturing system in view of Guo’s tool path to include the toolpath with a first tool as taught by Lovell et al. ‘961 in order to get the most efficient toolpath cutting times during the manufacturing of parts.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Sawyer et al. (US PG Pub. 2024/0354471 A1) in view of Guo (US PG Pub. 2022/0414293 A1) as applied to claims 1 and 11 above and in further view of Lovell et al. (US PG Pub. 2021/0034961 A1, hereinafter referred to as “Lovell et al. ‘961”) as applied to claims 2 and 12 above and in further view of Lovell et al. (US PG Pub. 2024/0061383 A1 hereinafter referred to as “Lovell et al. ’383”).
As per claims 3 and 13, Sawyer et al. in view of Guo and Lovell et al. ‘961 discloses the apparatus and method of claims 2 and 12, respectively. Sawyer et al. in view of Guo and Lovell et al. ‘961 does not further disclose, however, Lovell et al. ’383 discloses:
wherein the optimized part data is created by generating a first toolpath, and
wherein the first toolpath is generated by training a toolpath machine learning model based on toolpath training data (Lovell et al. ‘383: [0066] After training is finished, the machine learning algorithm can generate toolpaths that can be used to manufacture an object that is not among the training examples, or objects that the machine learning algorithm has not been trained with. Additional training examples that can represent one or more new objects (e.g., one or more new parts) can be obtained. The machine learning algorithm can be further trained with a combination of the existing training examples and the additional training examples. In some implementations, for the purpose of rapid training, the machine learning algorithm can be trained by performing fine-tuning based on a previously trained machine learning model, i.e., parameters of the machine learning model are updated from previously learned parameters, instead of calculated from scratch (e.g., random numbers or zeros). The toolpaths generated by the machine learning algorithm for these new parts can be further improved after the machine learning algorithm is trained with the addition of new training examples. In some implementations, the additional training examples can include data corresponding to user modifications to the toolpaths that a machine learning algorithm has previously generated. The data corresponding to user modifications can be used to train an improved machine learning algorithm that can generate more desirable toolpaths). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the manufacturing estimate system of Sawyer et al. in view of Guo’s commanding the tool motion path to machining the part in view of Lovell et al. ‘961’s tool path to include the machine learning as taught by Lovell et al. ‘383 in order to provide improved toolpaths and parts (Lovell et al. ‘383: [0066)
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sawyer et al. (US PG Pub. 2024/0354471 A1) in view of Guo (US PG Pub. 2022/0414293 A1) and Lovell et al. ’961, (US PG Pub. 2021/0034961 A1) as applied to claims 2 and 12 above and in further view of King et al. (US PG Pub. 20220214668 A1).
As per claims 4 and 14, Sawyer et al. in view of Guo and Lovell et al. discloses the apparatus and method of claims 2 and 12, respectively. Sawyer et al. in view of Guo does not further disclose, however, King et al. discloses:
wherein generating the optimized part model and optimized toolpath comprises generating the optimized part data automatically using a machine learning model trained on correlations between part data and manufacturability datum (King et al.: [0052],[0054]. The computation component 112 can also employ a machine learning method that may provide correlations found between different types of historical data to determine the similarity score. In various embodiments, the computation component 112 can determine the similarity score based on: metadata regarding the how parts are produced (e.g., manufacturing process chosen and/or tolerances required); and (King et al.: [0051] geometry of the part to be manufactured, which can be compared pointwise to one or more previously manufactured parts. Further, the metadata and/or geometry information can be combined with a weighted average to determine the similarity score). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the manufacturing estimate system of Sawyer et al. the manufacturing system in view of Guo’s tool path to include and Lovell et al.’s toolpath with a first tool to include machine learning models trained on correlations as taught by King et al. to in order to determine the similarity score/correlations based on manufacturing process chosen and/or tolerances required (King et al.: [0051]).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sawyer et al. (US PG Pub. 2024/0354471 A1) in view of Guo (US PG Pub. 2022/0414293 A1) as applied to claims 1 and 11 above and in further view of Lyu et al. (US PG Pub. 2017/0024497 A1).
As per claims 6 and 16, Sawyer et al. in view of Guo discloses the apparatus and method of claims 1 and 11, respectively. Sawyer et al. in view of Guo does further discloses, however, Lyu et al. discloses:
wherein the optimized part data comprises one or more altered variations of the 3D model (Lyu et al.: [0035] The computer-implemented framework 140 for the product design system may include structural analysis application software 146, manufacturing cost analysis application software 144, and the optimization module 142 implementing a MOEA. The framework 140 may iteratively define a plurality of variations of a simulated 3D design model by simultaneously performing a structural analysis and a cost analysis of each successive variation of the simulated design model. The framework may combine the results of performing the structural analysis and the cost analysis using the MOEA to arrive at a new variation of the simulated design model characterized by changes to both structural and cost parameters with respect to a previous variation of the simulated design model. The changes derived by the MOEA move both the structural and cost parameters closer to target goals and objectives for the design system than in the previous variation of the 3D design model. A Pareto distribution of the plurality of variations of the simulated 3D design model may be obtained by the framework to arrive at a desired design concept and simulated 3D design model for the product. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the manufacturing estimate system of Sawyer et al. the manufacturing system in view of Guo’s tool path to include variations of a 3d model as taught by to arrive at a new variation of the simulated design model characterized by changes to both structural and cost parameters with respect to a previous variation of the simulated design model (Lyu et al.: Abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FREDA A. NELSON whose telephone number is (571)272-7076. The examiner can normally be reached Monday-Friday, 10:00am - 6:30pm.
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, Shannon Campbell can be reached at 571-272-5587. 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.
/F.A.N/Examiner, Art Unit 3628 /SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628