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 .
This Office Action is in response to claims filed on 08/02/2023.
Claims 1-4 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/02/2023 is being considered by the examiner.
Drawings
The drawings are objected to because fig 4 is not clear and fig 2A, 2B, 6, 7, 8A 8B, 9, 10, 12A and 12B have no text labels. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-4 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 2 recites the limitation “when”. It’s not positively stated that the limitation will occur in the near future. Correction is required. For compact prosecution, Examiner is interpreting the claim as follows
wherein displacement is applied to joining areas between the individual parts of the three-dimensional model.
Dependent claims do not resolve the indefinite issue in the independent claim and thus are also rejected under 112(b) by virtue of their dependence on the rejected independent claim.
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 1-4 are rejected under 35 U.S.C. 103 as being unpatentable over Sandipan Karmakar, NPL “A Bayesian LASSO Algorithm for Simultaneous Variation Fault Diagnosis in I11 Conditioned Multistage Manufacturing”, Published: March 8-10, 2016, (hereafter Karmakar), in views of Abhishek Das, NPL, “Shape Variation modelling, analysis and statistical control for assembly system with compliant parts”, Published: February 2016 (hereafter Das).
Regarding claim 1. Karmakar teaches a production method for a completed component that is composed of a combination of a plurality of individual parts (Page 8, Fig 3, components combinations), the production method comprising:
a first step to determine an amount of change at evaluation sites of the completed component on a three-dimensional model of the completed component that occurs when displacement is applied to joining areas between the individual parts on the three-dimensional model (Page 3, Par 1, conjointly determine the product quality deviation measurements at station i);
a second step for extracting combinations of critical evaluation sites having a relatively large amount of change among the evaluation sites on the three-dimensional model and their corresponding critical joining areas among the joining areas on the three-dimensional model (Page 8, fig 3, measurement points) (Page 8, sec VI, final subassembly is measured at state 3, taken at 4 points);
a third step for generating regression models through Lasso regression using, as explanatory variables for each regression model (Page 3, equation 2 and 3), actual measurement data of joining areas of uncombined real individual parts corresponding to the critical joining areas (Page 2, equation 1, KPC dimensional measurement) (Page 3, equation 3, observation equation representing the measure) and using, as an objective variable for each regression model, actual measurement data of an evaluation site of a real completed component corresponding to the critical evaluation sites (Page 6, Par 1, equation 26,regression model are the solutions of the optimization problem );
a fourth step for performing Bayesian estimation using each regression model and relationship between the explanatory variables and the objective variable obtained using the actual measurement data to obtain partial regression coefficient probability distribution (Page 9, sec VII, in Bayesian analysis the variable selection is not a straightforward task, classification ability of the proposed Bayesian lasso model);
a fifth step for selecting an adjustment site of the individual parts based on mean and spread of the partial regression coefficient probability distribution if the measurement data of the evaluation sites of the completed component includes a value exceeding a permissible value (Page 9, mean of the Bayesian Lasso coefficients are shown in fig 6); and
a sixth step for producing the completed component using the individual parts in which the adjustment site has been adjusted (Page 8, Fig 4, complete component).
Karmakar does not teach performing finite element analysis.
Das teaches performing finite element analysis (Page 34, fig 2.5, compliant part measurement) (Page 174, KPCs are evaluated by using Finite Element Analysis (FEA)).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Karmakar to incorporate the teachings of Das to perform finite element analysis because it allows identification and characterization of real part shape errors that link design with manufacturing shape errors (Das, Page xviii, abstract).
Regarding claim 2. Karmakar teaches an evaluation site accuracy control method for a completed component that is composed of a combination of a plurality of individual parts (Page 8, Fig 3, components combinations), the evaluation site accuracy control method comprising:
a first step to determine an amount of change at evaluation sites of the completed component on a three-dimensional model of the completed component that occurs when displacement is applied to joining areas between the individual parts on the three-dimensional model (Page 3, Par 1, conjointly determine the product quality deviation measurements at station i);
a second step for extracting combinations of critical evaluation sites having a relatively large amount of change among the evaluation sites on the three-dimensional model and their corresponding critical joining areas among the joining areas on the three-dimensional model (Page 8, fig 3, measurement points) (Page 8, sec VI, final subassembly is measured at state 3, taken at 4 points);
a third step for generating regression models through Lasso regression using, as explanatory variables for each regression model (Page 3, equation 2 and 3), actual measurement data of joining areas of uncombined real individual parts corresponding to the critical joining areas (Page 2, equation 1, KPC dimensional measurement) (Page 3, equation 3, observation equation representing the measure) and using, as an objective variable for each regression model, actual measurement data of an evaluation site of a real completed component corresponding to the critical evaluation sites (Page 6, Par 1, equation 26,regression model are the solutions of the optimization problem );
a fourth step for performing Bayesian estimation using each regression model and relationship between the explanatory variables and the objective variable obtained using the actual measurement data to obtain partial regression coefficient probability distribution (Page 9, sec VII, in Bayesian analysis the variable selection is not a straightforward task, classification ability of the proposed Bayesian lasso model); and
a fifth step for selecting an adjustment site of the individual parts based on mean and spread of the partial regression coefficient probability distribution if the measurement data of the evaluation sites of the completed component includes a value exceeding a permissible value (Page 9, mean of the Bayesian Lasso coefficients are shown in fig 6).
Karmakar does not teach performing finite element analysis.
Das teaches performing finite element analysis (Page 34, fig 2.5, compliant part measurement) (Page 174, KPCs are evaluated by using Finite Element Analysis (FEA)).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Karmakar to incorporate the teachings of Das to perform finite element analysis because it allows identification and characterization of real part shape errors that link design with manufacturing shape errors (Das, Page xviii, abstract).
Regarding claim 3. Karmakar and Das teach the evaluation site accuracy control method according to claim 2, wherein in the second step, the critical evaluation sites and the critical joining areas are determined using the amount of change and stiffness relationship between the individual parts in the joining areas (Das, Page 56, equation 3.2, K stiffness matrices) (Das, Page 105, sec 4.4.2.2, stiffness and mass material properties).
Regarding claim 4. Karmakar and Das teach the evaluation site accuracy control method according to claim 2, wherein in the third step, Lasso regression is performed using a distance between two joint surfaces at each critical joining area and an amount of deviation of one of the joint surfaces from a reference position (Karmakar, Page 2, Par 1, variation analysis, deviations are very small as compared to the distance between them) (Karmakar, Page 3, equations 8 and 9, product quality deviation, functional relationship between variance of KCCs and KPCs ).
Conclusion
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/A.C./ Examiner, Art Unit 2189
/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189
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