DETAILED ACTION
Abstract
The abstract of the disclosure is objected to because it is unclear. Going forward with examination, the abstract is interpreted to be (Note that in applicant’s response, where a change is requested in the abstract, a separate page of the abstract containing the change will be needed):
--A method of assessing associated with each of a plurality locations of the component through the component. The generated data is passed to a machine learning branch where the generated data is compared to training data associated with each of the plurality of locations to determine whether the component is within a functionally tolerant dimension[[s]] at each of the plurality of locations. The manufactured component is accepted should the component be within the functionally tolerant dimension[[s]] at each of the plurality of locations, and the component is rejected if the component fails to be within the functionally tolerant dimension[[s]] .--
Correction is required. See MPEP § 608.01(b).
Specification
The disclosure (specification) is objected to because it is unclear. Going forward with examination, at least the following paragraphs are interpreted to be (Note that in applicant’s response, where a change is requested in the specification, an entire paragraph of the specification containing the change will be needed):
--[0024] In the physical testing branch 25, as shown in FIG. 3, a part definition 40 including design nominal geometry of the part in when it is cold, during dynamic test, stress and dynamic test, when it is hot during operation, etc., is defined by drawings and/or 3D models of the part. The part definition 40 is supplied to a part assessment app 26. Inspection data being part manufacturing raw data from manufacturing is supplied at 42 also to the part assessment app 26. The inspection data or manufacturing raw data may include nominal dimensions of the part as determined in the cold meshing (dimension inspection) at step 26 mentioned above. The inspection data or raw manufacturing data is then subjected to simulated stress test, test/evaluation at steps 30 and 28, etc. As mentioned above, these steps use solid mechanics simulations to simulate different dynamical and vibratory load cases that may be applied on the part in an actual operation, and are based on the geometry dimensions of the as determined in the cold meshing step.--
--[0025] As shown graphically at 44, the nominal dimensions of a part define a boundary 46. In the prior art of a part have been relied upon to indicate the part. Relying may therefore identify a number of parts as being unacceptable, when in fact they may actually be functionally tolerant parts. As shown graphically, a boundary 48 of a functionally tolerant part [[48]] may extend far beyond the nominal dimensions of the part defining the boundary 46.--
--[0029] FIG. 4 shows detail of the physical testing branch 25. A network 52 receives a part assessment data being the inspection data 24 from an operator or inspector 54. That inspection data 24 includes manufacturing raw data of the part. The inspection data 24 may include nominal dimensions, etc., of the part. The inspection data 24 is then delivered from the network 52 to a cloud function 57 where processes 25 and 35 of the testing system 20 shown in figures 2 and 3 may be performed. Network 52 may include computing devices that includes one or more computer processors, memory, storage means, network devices, input and/or output devices, and/or interfaces. The computing devices may be operable to execute one or more software programs. The computing devices may be operable to communicate with one or more networks established by one or more computing devices. The memory may include UVPROM, EEPROM, FLASH, RAM, ROM, DVD, CD, a hard drive, or other computer readable medium which may store data and/or the functionality of this description. The computing devices may be a desktop computer, laptop computer, smart phone, tablet, or any other computer device. Input devices of the network 52 may include a keyboard, mouse, touchscreen, etc. Output devices of the network 52 may include a monitor, speakers, printers, etc. Each of the computing devices may include one or more processors coupled to the memory. The computing devices may be coupled to each other by one or more connections [[40]]. The connection [[40]] may be a wired and/or wireless connection. The connection [[40]] may be established over one or more networks and/or other computing systems.
--[0030] The cloud function 57 has a rapid part assessment core 58 which receives raw data of the inspection data 24 from the network 52. Although disclosed on the cloud, this disclosure of the processes 25 and 35 of the testing system 20 could be run elsewhere. At 60 a stored workflow A, B or C is used for each type of assessed part or component that the operator or inspector 54 wants to assess/evaluate. The workflow A, B or C may be directed to a particular part or component that is being evaluated. Thus, step 60 may well include workflows or components.--
--[0031] The stored workflows A, B, C contain the part’s nominal geometry, functional and geometric limits, nominal mesh and loads, etc., to apply for dimension analysis and functional analysis, as well as the general ordering and orchestration of information of the individual steps contained in the workflows. The stored workflow A, B, C may therefore be a core set of functionalities that are valid for any type of part or component to be assessed/evaluated by this system 20.--
--[0032] After this step 60 where the stored workflows have assessed/evaluated or analyzed the part or component for cold meshing at 26 (cold dimension inspection), dynamic test at 28, and/or stress and dynamic test at 30, and/or hot meshing at 34 (hot dimension inspection at operating conditions), etc., assessment or analysis of the part or component by the testing branch 25 may be sent back to the operator or inspector 54 at step 62 and/or are stored at step 64 for long term storage, and also for status update and reporting.
--[0033] Moving to FIG. 5, the machine learning branch 35 initially verifies the stored data by using the machine learning module 36 at step 66 for the part or component to be assessed, the stored data being the part definition 40 including the design nominal dimensions of the part or component as defined by the drawings and/or 3D models of the part or component. The curvature and profile at each of a plurality of section cuts at a plurality of locations of the part or component through the part or component as-designed are then extracted from the part definition 40 at step 68. As an example, there may be on the order of three dozen section cuts in each airfoil 27. As mentioned above, the broader aspects of this disclosure extend beyond looking at sections.--
--[0035] As shown, the scope check 70 may identify a particular part or component as being an outlier at 80 based on a comparison between the results of the assessment of the part or component by the physical testing branch 25 and the part definition 40 including the design nominal dimensions of the part or component as a whole. If a result of the comparison shows a clear difference, the result is communicated to the labeling task 72 and a final classification task 82. At task 72, a label is added, so as to allow a human to identify and re-evaluate the particular part or component visually. At task 82, a final determination for the part or component is indicated as an outlier at 84 automatically.--
--[0036] Assuming the scope check 70 does not identify a part or component as a whole to be clearly at least several sections of the part or component at 88. Then, at step 90 there is post processing to determine likelihood of an outlier based on the inference. The inference compares the results of the assessment of the part or component by the physical testing branch 25 of the at least several sections with the part definition 40 of the same at least several sections to determine a number of matches, so as to determine the likelihood of an outlier. For instance, a low number of matches would indicate a higher likelihood for the part or component to be an outlier. Conversely, a high number of matches would indicate a less likelihood for the part or component to be an outlier, i.e., the part of component is good. After that, based on the number of matches, the part or component is identified with a percentage of likelihood of being an outlier as between zero match (clearly bad) at 92; one match (maybe bad) at step 94; two matches (maybe good) at step; and three matches (clearly good) at step 98. Of course, these number are examples and other quantities may be used. A percentage chance for each of these four likelihoods is then stored in data storage at 100.--
--[0037] There is then a component review at 102 that has a step 104 that identifies whether to accept 108 or reject 106 the part and/or the component based upon the analysis or assessment.--
--[0040] At 110 there is a graphical showing of the results for a number of evaluated parts or components based upon the determined dimension and assessment results.--
--[0041] In the machine learning branch 35, as shown in FIG. 6, a K-fold 138 for a part or component to be evaluated folds and splits training data set 114 into, e.g., five equal K 128, 130, 132, 134, 136 and five splits 118, 120, 122, 124, 126. All folds must have comparable data distribution.--
--[0042] Each of the five folds 128, 130, 132, 134 and 136 for each of the splits 118, 120, 122, 124 and 126 are samples of the training data set 114 to be trained on data 112. The data in each of the folds is distinctand different between training data samples/sets and validation data samples/sets. Thus, each fold represents a distinct training data set to train a distinct machine learning model or a validation data set to validate the trained machine learning model for a particular model part or component. Only the training data set 114 is split between the folds.--
--[0043] As shown in FIG. 6 again, K models are trained on distinct datasets from each of the K folds, but all K models are test validated on a common test validation dataset.--
--[0044] An example test will be explained. At the start, you have 100% of data of original geometry or shape of a common part or component as-manufactured in a simulated operation of the part or component, that you split into two sets: training data set of 80% and test validation data set of 20%. You set the test validation data set aside. The remaining 80% of data being the training data set is then folded into 5 folds, each containing 16% of the original data set from the same blade is ensured to be only contained in a single fold of 16% (to avoid contamination). The machine learning model is then trained 5 times as follows. Choose 4 out of the 5 folds, and call them train data, and use the remaining fold as test validation data. Train a model by using the train data. Verify performance of the trained model by using the test validation data set that was originally put aside (20%). Repeat the process 4 other times, by continuously and alternatingly taking 4 out of the 5 folds at a time and using 1 remaining fold as validation. After 5 iterations, you ensured that each of the folds had a chance to be used as validation.--
--[0048] Each of the plural folds receive training data for each of the plurality of splits, but does not receive test validation data for each of the plurality of splits.--
--[0057] The use of a probability identifier of whether the part is good or bad, and weighting the final determination in a conservative manner is disclosed and claimed in copending patent application serial number 18/594,862, entitled "RAPID PART ASSESSMENT DELOPING GOOD/BAD PERCENTAGE LIKELIHOOD" filed on even date herewith and owned by the assignee of this application.--
(Note that Applicant inadvertently amended Par. 0049 as Par. 0057)
Appropriate correction is required.
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 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5-15 and 18-20 of copending Application No. 18/594,862 (hereinafter “reference application,” printed in Pub. No. US 2025/0276814 A1). Although the claims at issue are not identical, they are not patentably distinct from each other because the reference application already claims at least the following (This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented):
1. A method of assessing the quality of a manufactured component comprising the steps of (See the reference application claim 1):
manufacturing a component and generating data associated with the component at each of a plurality locations through the component;
passing the generated data to a machine learning branch wherein the generated data is compared to training data associated with each of the plurality of locations to determine whether the component is within functionally tolerant dimensions at each of the plurality of locations; and
accepting the component should the component be within the functionally tolerant dimension at each of the plurality of locations, and rejecting the component if the component fails to be within the functionally tolerant dimensions, at each of the plurality of locations.
2. The method as set forth in claim 1, wherein the plurality of locations is a plurality of cross-section that include curvature and dimensional measurements at each of the plurality of sections (See the reference application claim 3).
3. The method as set forth in claim 2, wherein the component has an airfoil (See the reference application claim 2).
4. The method as set forth in claim 3, wherein the plurality of locations includes sections through the airfoil (See the reference application claim 5).
5. The method as set forth in claim 3, wherein the component is an integrally bladed rotor (See the reference application claim 6).
6. The method as set forth in claim 1, wherein functionally tolerant components are identified which have dimensions outside of a nominal tolerance range (See the reference application claim 7).
7. A method of assessing quality of a manufactured component comprising the steps of:
manufacturing a component and generating data associated with the component at each of a plurality locations through the component;
passing the generated data to a machine learning branch wherein the generated data is compared to training data associated with each of the plurality of locations to determine whether the component is of functionally tolerant dimensions at each of the plurality of locations;
accepting the component should the component be within the functionally tolerant dimensions at each of the plurality of locations, and rejecting the component if the component fails to be within the functionally tolerant dimensions, at each of the plurality of locations; and
wherein the machine learning branch utilizes K-fold validation with plural folds at each of the plurality of locations (See the reference application claims 1 and 8).
8. The method as set forth in claim 7, wherein the plural folds are provided with training data from prior assessments (See the reference application claim 9).
9. The method as set forth in claim 7, wherein each of the plural folds receive training data from a common part, but the training data across the plural folds is distinct (See the reference application claim 10).
10. The method as set forth in claim 1, wherein an evaluation is reached as to a percentage chance that the component is acceptable and a percentage chance that the component is rejectable at each of the location (See the reference application claim 11).
11. A system for component assessment comprising (See the reference application claim 13):
processing circuitry operable to assess the quality of a manufactured component by receiving generated data associated with the component at each of a plurality of locations of the component through;
also operable for passing the generated data to a machine learning branch where the generated data is compared to training data associated with each of the plurality of locations to determine whether the component is within a functional tolerant dimension at each of the plurality of locations; and
also operable for accepting the component should the component be within the functionally tolerant dimension at each of the plurality of locations, and rejecting the component if the component fails to be within functionally tolerant dimensions.
12. The system as set forth in claim 11, wherein the component includes an airfoil (See the reference application claim 14).
13. The system as set forth in claim 12, wherein the generated data includes curvature and dimensional measurements (See the reference application claim 15).
14. The system as set forth in claim 13, wherein functionally tolerant components are identified which have dimensions outside of a nominal tolerance range (See the reference application claims 1 and 7).
15. The system as set forth in claim 12, wherein the component is an integrally bladed rotor (See the reference application claim 18).
16. The system as set forth in claim 12, wherein the machine learning branch utilizes K-fold validation at each of the plural folds at each of the plurality of locations through the airfoil (See the reference application claim 19).
17. The system as set forth in claim 16, wherein the plural folds are provided with training data from prior assessments (See the reference application claim 19).
18. The system as set forth in claim 16, wherein each of the plural folds receive training data from a common part (See the reference application claims 1, 9 and 10, which are directed to a method necessitating all the structures of the system being claimed).
19. The system as set forth in claim 11, wherein an evaluation is reached as to a percentage chance that the component is acceptable and a percentage chance the component is rejectable based on a comparison between the generated data and the training data at each of the plurality of locations (See the reference application claim 20).
20. The system as set forth in claim 19, wherein the evaluation uses a conservative evaluation such that if the percentage chance the component being rejectable based on the comparison between the generated data and the training data at each of the plurality of locations exceeds a predetermined maximum that is less than 50%, the component is rejected (See the reference application claims 1, 11 and 12, which are directed to a method necessitating all the structures of the system being claimed).
Claims 1-19 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 and 15-20 of copending Application No. 18/594,778 (hereinafter “reference application,” printed as Pub. No. US 2025/0276812 A1). Although the claims at issue are not identical, they are not patentably distinct from each other because the reference application already claims at least the following (This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented):
1. A method of assessing the quality of a manufactured component comprising the steps of (See the reference application claim 1):
manufacturing a component and generating data associated with the component at each of a plurality locations through the component;
passing the generated data to a machine learning branch wherein the generated data is compared to training data associated with each of the plurality of locations to determine whether the component is within functionally tolerant dimensions at each of the plurality of locations; and
accepting the component should the component be within the functionally tolerant dimension at each of the plurality of locations, and rejecting the component if the component fails to be within the functionally tolerant dimensions, at each of the plurality of locations.
manufacturing a component and generating data at each of a plurality locations through the component;
passing the generated data to a machine learning branch wherein the generated data is compared to training data at each of the plurality of locations to determine whether the component is of functionally tolerant dimensions at each of the plurality of locations; and
accepting the manufactured component should the component be within functionally tolerant dimensions at each of the plurality of locations, and rejecting the component if the component fails to be within the functionally tolerant dimensions respectively, at each of the plurality of locations.
2. The method as set forth in claim 1, wherein the plurality of locations is a plurality of cross-section that include curvature and dimensional measurements at each of the plurality of sections (See the reference application claim 3).
3. The method as set forth in claim 2, wherein the component has an airfoil (See the reference application claim 2).
4. The method as set forth in claim 3, wherein the plurality of locations includes sections through the airfoil (See the reference application claim 4).
5. The method as set forth in claim 3, wherein the component is an integrally bladed rotor (See the reference application claim 5).
6. The method as set forth in claim 1, wherein functionally tolerant components are identified which have dimensions outside of a nominal tolerance range (See the reference application claim 6).
7. A method of assessing quality of a manufactured component comprising the steps of:
manufacturing a component and generating data associated with the component at each of a plurality locations through the component;
passing the generated data to a machine learning branch wherein the generated data is compared to training data associated with each of the plurality of locations to determine whether the component is of functionally tolerant dimensions at each of the plurality of locations;
accepting the component should the component be within the functionally tolerant dimensions at each of the plurality of locations, and rejecting the component if the component fails to be within the functionally tolerant dimensions, at each of the plurality of locations; and
wherein the machine learning branch utilizes K-fold validation with plural folds at each of the plurality of locations (See the reference application claims 1 and 7).
8. The method as set forth in claim 7, wherein the plural folds are provided with training data from prior assessments (See the reference application claim 8).
9. The method as set forth in claim 7, wherein each of the plural folds receive training data from a common part, but the training data across the plural folds is distinct (See the reference application claim 9).
10. The method as set forth in claim 1, wherein an evaluation is reached as to a percentage chance that the component is acceptable and a percentage chance that the component is rejectable at each of the location (See the reference application claim 10).
11. A system for component assessment comprising (See the reference application claim 11):
processing circuitry operable to assess the quality of a manufactured component by receiving generated data associated with the component at each of a plurality of locations across the component through;
also operable for passing the generated data to a machine learning branch where the generated data is compared to training data associated with each of the plurality of locations to determine whether the component is within functional tolerant dimension at each of the plurality of locations; and
also operable for accepting the component should the component be within the functionally tolerant dimension at each of the plurality of locations, and rejecting the component if the component fails to be within the functionally tolerant dimensions.
12. The system as set forth in claim 11, wherein the component includes an airfoil (See the reference application claim 12).
13. The system as set forth in claim 12, wherein the generated data includes curvature and dimensional measurements (See the reference application claim 13).
14. The system as set forth in claim 13, wherein functionally tolerant components are identified which have dimensions outside of a nominal tolerance range (See the reference application claim 16).
15. The system as set forth in claim 12, wherein the component is an integrally bladed rotor (See the reference application claim 15).
16. The system as set forth in claim 12, wherein the machine learning branch utilizes K-fold validation at each of the plural folds at each of the plurality of sections through the airfoil (See the reference application claim 17).
17. The system as set forth in claim 16, wherein the plural folds are provided with training data from prior assessments (See the reference application claim 18).
18. The system as set forth in claim 16, wherein each of the plural folds receive training data from a common part (See the reference application claim 19).
19. The system as set forth in claim 11, wherein an evaluation is reached as to a percentage chance that the component is acceptable and a percentage chance the component is rejectable based on a comparison between the generated data and the training data at each of the plurality of locations (See the reference application claim 20).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Specifically, independent claims 1, 7 and 11 recite a term “a functionally tolerant dimension.” The disclosure however fails to reasonably convey said term. Nowhere in the disclosure “functionally tolerant dimension” is defined. The remaining claims fall together with the independent claims.
Going forward with examination, at least claims 1, 7 and 11 are interpreted to be:
--1. A method of assessing quality of a manufactured component comprising the steps of:
manufacturing a component and generating dimension data associated with the component at each of a plurality of locations of the component through the component;
passing the generated dimension data to a machine learning branch where the generated dimension data is compared to training data associated with each of the plurality of locations to determine whether the component is within a which would allow the component to be functional; and
accepting the component should the component be within the the
--7. A method of assessing quality of a manufactured component comprising the steps of:
manufacturing a component and generating dimension data associated with the component at each of a plurality of locations of the component through the component;
passing the generated dimension data to a machine learning branch where the generated dimension data is compared to training data associated with each of the plurality of locations to determine whether the component is within a which would allow the component to be functional; and
accepting the component should the component be within the the
wherein the machine learning branch utilizes K-fold validation with plural folds at each of the plurality of locations.--
--11. A system for component assessment comprising:
processing circuitry operable to assess dimension data associated with the component at each of a plurality of locations of the component through the component;
also operable for passing the generated dimension data to a machine learning branch where the generated dimension data is compared to training data associated with each of the plurality of locations to determine whether the component is within a that would allow the component to be functional; and
also operable for accepting the component should the component be within the at each of the plurality of locations.--
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Baeuerle et al. (US 2025/0155853 A1; hereinafter “Baeuerle”).
1. Baeuerle teaches a method of assessing quality of a manufactured component (B) comprising the steps of (See figs. 1, 2, reproduced and annotated below):
manufacturing (by using a production process 1 which runs on one or more process parameters P) a component B (which may be shape component; Par. 0035) and generating dimension data (M) associated with the component (B) at each of a plurality of locations of the component (B) through the component B (the dimension data M may include, for example, geometry dimensions along with dimensional tolerances, surface condition, etc., of the component B; Pars. 0022, 0051. Note that the geometry dimensions along with dimensional tolerances are obviously taken from a plurality of locations of the component B through the component B being a shape component for example);
passing the generated dimension data (M) to a machine learning branch (= quality model 12 which may be a neural network; Par. 0023) where the generated dimension data (M) is compared to (so as to map with) training data (G) associated with each of the plurality of locations to determine whether the component (B) is within a tolerant dimension at each of the plurality of locations which would allow the component (B) to be functional (i.e., to be usable for an intended use, and may include manufacturing tolerances, dimensions, a surface quality, etc.; Pars. 0044-0045); and
using a result of the determination to train a process parameter model 11 to adjust and output the one or more process parameters P (Abstract; Par. 0057).
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Baeuerle is silent about a recited step: accepting the component (B) should the component (B) be within the tolerant dimension at each of the plurality of locations, and rejecting the component (B) if the component (B) fails to be within the tolerant dimension at each of the plurality of locations.
However, it appears that the feature may and can easily be done solely by a human, thus appears to be unpatentable. For instant, an operator, inspector, or a user of Baeuerle method, may and ought to be allowed to accept the component (B) should the component (B) be within the tolerant dimension at each of the plurality of locations, and to reject the component (B) if the component (B) fails to be within the tolerant dimension at each of the plurality of locations, in order to always have the component (B) good for use.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the present application to have the method include accepting the component (B) should the component (B) be within the tolerant dimension at each of the plurality of locations, and rejecting the component (B) if the component (B) fails to be within the tolerant dimension at each of the plurality of locations, in order to always have the component (B) good for use.
2. Baeuerle as modified teaches the method as set forth in claim 1, but is silent about a feature: wherein the plurality of locations is a plurality of cross-sections that include curvatures and dimensional measurements at each of the plurality of sections.
However, Baeuerle appears to teach the plurality of locations to be any plurality of locations. It’s also up to the operator, inspector, or a user of the method, to include any plurality of locations, including but not limited to a plurality of cross sections that include curvatures and dimensional measurements at each of the plurality of sections. Baeuerle method as modified also appears capable of assessing quality of the component (B) where the plurality of locations are a plurality of cross sections that include curvatures and dimensional measurements at each of the plurality of sections.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the present application to have the plurality of locations be a plurality of cross-sections that include curvatures and dimensional measurements at each of the plurality of sections, in order to include the geometry dimensions along with dimensional tolerances, surface condition, etc., of the component (B).
3. Baererle as modified teaches the method as set forth in claim 2, wherein the component (B) has an airfoil (or anything. Note that the claim appears to recite an intended use of the method to be used on an airfoil. An intended use is unpatentable).
4. Baererle as modified teaches the method as set forth in claim 3, wherein the plurality of locations includes sections through the airfoil (as is obvious from the discussions above in claims 1-3).
5. Baererle as modified teaches the method as set forth in claim 3, wherein the component is an integrally bladed rotor (or anything. Note that the claim appears to recite an intended use unpatentable).
6. Baererle as modified teaches the method as set forth in claim 1, wherein functionally tolerant components are identified which have dimensions outside of a nominal tolerance range (as is obvious from the discussion above in claim 1).
10. Baererle as modified teaches the method as set forth in claim 1, wherein an evaluation is reached as to a percentage chance that the component is acceptable and a percentage chance that the component is rejectable at each of the location.
Note that, similar to the discussion above in claim 1, it appears that the recited feature may and can easily be done solely by a human thus appears to be unpatentable. For instant, an operator, inspector, or a user of Baeuerle method, may and ought to be allowed to accept the component B when an evaluation is reached as to a percentage chance that the component is acceptable at each of the location and to reject the component B when an evaluation is reached as to a percentage chance that the component is rejectable at each of the location. The claim fails to add any further method step to claim 1, especially any evaluation step and what would be included in said evaluation step.
Allowable Subject Matter
Claims 7-9 and 11-20 would be allowable if the above objections, double patenting rejections, and the 112 rejections, were overcome. The following would be a statement for indication of an allowable subject matter (which was also indicated in the Office action of 2/26/2026):
With respect to claim 7, prior art of record doesn’t teach, suggest, or render obvious the total combination of the recited features, including the following allowable subject matter: “wherein the machine learning branch utilizes K-fold validation with plural folds at each of the plurality of locations.”
(Claims 8-9 are dependent on claim 7.)
With respect to claim 11, prior art of record doesn’t teach, suggest, or render obvious the total combination of the recited features, including the following allowable subject matter:
“processing circuitry operable to assess quality of a manufactured component (B) by receiving generated dimension data associated with the component at each of a plurality of locations of the component through the component;
also operable for passing the generated dimension data to a machine learning branch where the generated dimension data is compared to training data associated with each of the plurality of locations to determine whether the component is within a tolerant dimension at each of the plurality of locations which would allow the component to be functional; and
also operable for accepting the component should the component be within the tolerant dimension at each of the plurality of locations, and rejecting the component if the component fails to be within the tolerant dimension at each of the plurality of locations.”
(Claims 12-20 are dependent on claim 11.)
Claims 16-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims (provided that the above objections, double patenting rejections, and 112 rejections, were overcome). The following would be a statement for indication of an allowable subject matter (which is essentially equivalent to the allowable subject matter for claim 7):
With respect to claim 16, prior art of record doesn’t teach, suggest, or render obvious the total combination of the recited features, including the following allowable subject matter: “wherein the machine learning branch utilizes K-fold validation at each of the plural folds at each of the plurality of locations through the airfoil.”
(Claims 17-18 are dependent on claim 16.)
Response to Arguments
Applicant's arguments filed with the amendment on 3/19/2026 have been fully considered but they are not persuasive in part.
Abstract and Specification: Applicant argues that the examiner’s interpretations of the abstract and specification would change applicant’s disclosure, for example, paragraph 23. The examiner agrees with applicant that paragraph 23 may remain unchanged. Applicant however fails to point out any other paragraph(s) which the examiner’s interpretations were incorrect. Therefore, those other paragraphs would still need to be amended to at least correct the pointed-out syntax, grammatical, antecedent errors, references to the drawings, etc. It appears that applicant has amended the independent claims based on the examiner’s interpretations of the abstract and specification.
Double Patenting Rejections: Applicant appears to agree with the examiner regarding the double patenting rejections, but fails to file proper terminal disclaimer(s) to alleviate the rejections.
112 Rejections: Applicant fails to address and/or amend the claims to alleviate the 112 rejections.
103 Rejections: Claims 1-6 and 10 remain rejected (as discussed above). There is no control/controller recited in the claims, so applicant’s arguments about a “control” and “programming” are irrelevant. Naturally, a human may, and ought to allowed to, accept or reject the component (B) in order to always have the component (B) good for use.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nguyen (Wyn) Q. Ha whose telephone number is (571) 272-2863, email: nguyenq.ha@uspto.gov. The examiner can normally be reached Monday - Friday 8 am - 4:30 pm (Eastern Time).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Meier can be reached at (571) 272-2149. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Nguyen Q. Ha/Primary Examiner, Art Unit 2853 March 28, 2026
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