DETAILED ACTION
This Office Action is a first Office Action on the merits of the application. Claims 1 - 28 are presented for examination. Claims 1 - 11, 13, 14, 16, and 17 are rejected.
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 .
Drawings Objections
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: Element 108 on page 14, line 26, elements 300 and 306 on page 29, lines 27 and 29 (elements 306a, 306b, and 306c are shown, but not “306”), elements 318 and 320 on page 30, lines 26 - 27, and element 412 on page 32, lines 5 and 7 are not disclosed in the drawings. 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. 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.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: Elements 410, 431, 460 in FIG. 4 and element 908 in FIG. 9 are not disclosed in the specification. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) 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. 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.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “912” has been used to designate both a work profile in FIG. 9 and a pressure curve in FIG.10. 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. 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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: an input layer includes a plurality of nodes operative to and a plurality of layers are operative to produce in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 - 11 and 17 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 7 and 17 lack antecedent basis for “the group consisting of” (Claim 7, line 2, claim 17, line 3).
Suggested language: Amend the phrase to recite “a group consisting of”.
For claim 1: Claim limitations “an input layer includes a plurality of nodes operative to receive measurement data comprising a work-profile data set representing a mechanical response over a displacement to a cold-working material-processing operation effecting the displacement, by a target portion of a workpiece” and “a plurality of layers are operative to produce activations of nodes based on feature sets derived from the measurement data, and to further produce an output based on the activations, the output representing an assessment of performance of the cold-working material-processing operation” in claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. In particular, the input layer is part of the deep feature-recognition network engine (see page line 9), but the deep feature-recognition network engine is not disclosing corresponding structure, material, or acts to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the functions of the input layer and the plurality of layers. As would be recognized by those of ordinary skill in the art, the input layer operative to receive and the plurality of layers operative to produce can be performed in any number of ways in hardware, software, or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform the claimed functions. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Dependent claims 2 - 11 are rejected due to inherited claim deficiencies of claim 1.
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 1 - 11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains 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, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed functions recited. The specification does not demonstrate that applicant has made an invention that achieves the claimed functions because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Dependent claims 2 - 11 are rejected due to inherited claim deficiencies of claim 1.
Suggested language: Page 9 recites a computer system including one or more processors and memory. It is recommended the system claim in claim 1 includes these features while providing amendments to overcome the 112 issues regarding the “input layer…operative to” and “plurality of layers are operative to” language.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 6, 7, 11, 13, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (“Deep learning in Sheet Metal Bending with a Novel Theory-Guided DNN”), hereinafter “Liu”, and further in view of Toktas et al. (“Artificial Neural Networks Solution to Display Residual Hoop Stress Field Encircling a Split-Sleeve Cold Expanded Aircraft Fastener Hole”), hereinafter “Toktas”.
As per claim 1, Liu discloses:
a system for automated assessment of material-processing operations (Liu, page 567, left column, lines 43 - 46 discloses using CAD data for a workpiece to train a deep neural network for predicting a process pertaining to sheet metal.)
the system comprising a first deep feature-recognition network engine including a multilayer architecture
(Liu, page 567, right column, lines 20 - 26 and 30 - 33 discloses a deep learning method applied to metal forming, including sheet metal and DNN (deep neural network) architecture.)
Liu does not expressly disclose:
wherein an input layer includes a plurality of nodes operative to receive measurement data comprising a work-profile data set representing a mechanical response over a displacement to a cold-working material-processing operation effecting the displacement, by a target portion of a workpiece; and
a plurality of layers are operative to produce activations of nodes based on feature sets derived from the measurement data, and to further produce an output based on the activations, the output representing an assessment of performance of the cold-working material-processing operation.
Toktas however discloses:
wherein an input layer includes a plurality of nodes operative to receive measurement data comprising a work-profile data set representing a mechanical response over a displacement to a cold-working material-processing operation effecting the displacement, by a target portion of a workpiece (Toktas, page 555, right column, lines 45 - 50 discloses data for a neural network, including strain values measured to produce residual stress patterns and after cold expansion was performed to increase the hole size by a material removal, changes regarding micro-strain were obtained, with outcomes used as input for the neural network.)
a plurality of layers are operative to produce activations of nodes based on feature sets derived from the measurement data (Toktas, FIG. 1 discloses the neural network with a plurality of layers, with page 556, left column, lines 34 through right column, lines 1 - 3 adds the input layer provided with data pertaining to thickness direction, radial distance from the hole, and angular variation around the hole, with residual type of stress used as an output layer, along with page 555, right column, lines 2 - 14 discloses a transfer or activation function deciding the output of a neuron based on the input from a summation function evaluating the input, with a sigmoid type of function as a type of transfer or activation function, and page 556, right column, lines 15 - 21 adds the sigmoid transfer function used with an algorithm to train and test the network with different number of layers.)
to further produce an output based on the activations, the output representing an assessment of performance of the cold-working material-processing operation (Toktas, page 559, left column, lines 3 - 18 discloses results, including outcomes of the network including different number of neurons available in the hidden layer, with results listed and compared in Tables 1 - 3.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine training a neural network regarding a process performed on a workpiece teaching of Liu with training a neural network using data of a workpiece after a cold expansion process teaching of Toktas. The motivation to do so would have been because Toktas discloses the benefit of using artificial neural network (ANN) to provide realistic and better stress distribution information in areas with fluctuating residual strain gradient (Toktas, page 561, right column, lines 34 - 38.)
As per claim 13, Liu discloses:
at least one non-transitory machine-readable storage medium containing instructions that, when executed by a computer system, cause the computer system to execute operations to assess of material-processing operations (Liu, page 567, left column, lines 43 - 46 discloses using CAD data for a workpiece to train a deep neural network for predicting a process pertaining to sheet metal.)
Liu does not expressly disclose:
the operations comprising receiving, by an input layer of a deep feature-recognition network, measurement data comprising a work-profile data set representing a mechanical response over a displacement to a cold-working material-processing operation effecting the displacement, by a target portion of a workpiece;
producing activations of nodes of a plurality of additional layers of the deep feature-recognition network based on feature sets derived from the measurement data, and
producing an output based on the activations, the output representing an assessment of performance of the cold-working material-processing operation.
Toktas however discloses:
the operations comprising receiving, by an input layer of a deep feature-recognition network, measurement data comprising a work-profile data set representing a mechanical response over a displacement to a cold-working material-processing operation effecting the displacement, by a target portion of a workpiece (Toktas, page 555, right column, lines 45 - 50 discloses data for a neural network, including strain values measured to produce residual stress patterns and after cold expansion was performed to increase the hole size by a material removal, changes regarding micro-strain were obtained, with outcomes used as input for the neural network.)
producing activations of nodes of a plurality of additional layers of the deep feature-recognition network based on feature sets derived from the measurement data (Toktas, FIG. 1 discloses the neural network with a plurality of layers, with page 556, left column, lines 34 through right column, lines 1 - 3 adds the input layer provided with data pertaining to thickness direction, radial distance from the hole, and angular variation around the hole, with residual type of stress used as an output layer, along with page 555, right column, lines 2 - 14 discloses a transfer or activation function deciding the output of a neuron based on the input from a summation function evaluating the input, with a sigmoid type of function as a type of transfer or activation function, and page 556, right column, lines 15 - 21 adds the sigmoid transfer function used with an algorithm to train and test the network with different number of layers.)
producing an output based on the activations, the output representing an assessment of performance of the cold-working material-processing operation (Toktas, page 559, left column, lines 3 - 18 discloses results, including outcomes of the network including different number of neurons available in the hidden layer, with results listed and compared in Tables 1 - 3.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine training a neural network regarding a process performed on a workpiece teaching of Liu with training a neural network using data of a workpiece after a cold expansion process teaching of Toktas. The motivation to do so would have been because Toktas discloses the benefit of using artificial neural network (ANN) to provide realistic and better stress distribution information in areas with fluctuating residual strain gradient (Toktas, page 561, right column, lines 34 - 38.)
For claim 6: The combination of Liu and Toktas discloses claim 6: The system of claim 1, wherein
the measurement data includes nominal geometry or operation type information of the cold-working material-processing operation (Toktas, page 555, left column, lines 56 - 57 discloses data collected for the input, with FIG. 1 showing the input layer of the neural network including a thickness, radial distance, and angular variation.)
wherein the first deep feature-recognition network engine includes a second input layer operative to receive the nominal geometry or operation type information (Toktas, FIG. 1 discloses a layer in the form of a hidden layer to receive the data of the input data.)
and wherein the plurality of layers are operative to produce at least a portion of the activations based in part on the nominal geometry or operation type information (Toktas, page 555, right column, lines 2 - 14 discloses a transfer or activation function deciding the output of a neuron based on the input from a summation function evaluating the input, with a sigmoid type of function as a type of transfer or activation function, and page 556, right column, lines 15 - 21 adds the sigmoid transfer function used with an algorithm to train and test the network with different number of layers.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine training a neural network regarding a process performed on a workpiece teaching of Liu with training a neural network using data of a workpiece after a cold expansion process teaching of Toktas, and the additional teaching of an input layer, activation functions on neuron for an output and a hidden layer, also found in Toktas. The motivation to do so would have been because Toktas discloses the benefit of using artificial neural network (ANN) to provide realistic and better stress distribution information in areas with fluctuating residual strain gradient (Toktas, page 561, right column, lines 34 - 38.)
For claim 7: The combination of Liu and Toktas discloses claim 7: The system of claim 6, wherein
the nominal geometry or operation type information includes at least one data type selected from the group consisting of:
a hole diameter, type of tooling associated with the cold-working material-processing operation, a material thickness of a workpiece, construction of the workpiece, a type of workpiece material, a tooling actuation parameters, or any combination thereof (Toktas, FIG. 1 discloses thickness as an input to the neural network, and page 55, right column, lines 27 - 30 discloses an aluminum alloy plate with a diameter size used for the cold expansion process.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine training a neural network regarding a process performed on a workpiece teaching of Liu with training a neural network using data of a workpiece after a cold expansion process teaching of Toktas, and the additional teaching of a type of material, thickness, and diameter size for a cold expansion process, also found in Toktas. The motivation to do so would have been because Toktas discloses the benefit of using artificial neural network (ANN) to provide realistic and better stress distribution information in areas with fluctuating residual strain gradient (Toktas, page 561, right column, lines 34 - 38.)
As per claim 17, note the rejections of claim 7 above. The instant claim 17 recite substantially the same limitations as the above rejected claim 7 and are therefore rejected under the same prior art teachings.
For claim 11: The combination of Liu and Toktas discloses claim 11: The system of claim 1, wherein the output comprises at least one of:
a binary assessment of a performance of the cold-working material- processing operation indicating whether the operation meets specifications; or
a measure of a performance of the cold-working material-processing operation indicating a degree to which the operation meets specifications (Toktas, page 555, right column, lines 22 - 27 discloses cold expansion data used for neural network modeling, and page 559, right column, lines 8 - 10 through page 560, left column, lines 1 - 2 discloses the neural network process regarding residual stress data, in which the images at any orientation is shown to be at an acceptable precision.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine training a neural network regarding a process performed on a workpiece teaching of Liu with training a neural network using data of a workpiece after a cold expansion process teaching of Toktas, and the additional teaching of the cold expansion and neural network process providing acceptable results, also found in Toktas. The motivation to do so would have been because Toktas discloses the benefit of using artificial neural network (ANN) to provide realistic and better stress distribution information in areas with fluctuating residual strain gradient (Toktas, page 561, right column, lines 34 - 38.)
For claim 16: The combination of Liu and Toktas discloses claim 6: The at least one non-transitory machine-readable medium of claim 13, wherein
the measurement data includes nominal geometry or operation type information of the cold-working material-processing operation (Toktas, page 555, left column, lines 56 - 57 discloses data collected for the input, with FIG. 1 showing the input layer of the neural network including a thickness, radial distance, and angular variation.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine training a neural network regarding a process performed on a workpiece teaching of Liu with training a neural network using data of a workpiece after a cold expansion process teaching of Toktas, and the additional teaching of thickness, angular radiation, and radial distance information pertaining to input for a neural network, also found in Toktas. The motivation to do so would have been because Toktas discloses the benefit of using artificial neural network (ANN) to provide realistic and better stress distribution information in areas with fluctuating residual strain gradient (Toktas, page 561, right column, lines 34 - 38.)
Claims 2, 4, 5, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (“Deep learning in Sheet Metal Bending with a Novel Theory-Guided DNN”), in view of Toktas et al. (“Artificial Neural Networks Solution to Display Residual Hoop Stress Field Encircling a Split-Sleeve Cold Expanded Aircraft Fastener Hole”), and further in view of Ross et al. (U.S. PG Pub 2013/0200543 A1), hereinafter “Ross”.
As per claim 2, the combination of Liu and Toktas discloses the system of claim 1.
The combination of Liu and Toktas does not expressly disclose:
wherein the work-profile data set includes a measured force or pressure as a function of displacement of an actuator effecting the cold-working material-processing operation that includes cold expansion of a hole at the target portion of the workpiece to impart residual stress to the workpiece.
Ross however discloses:
wherein the work-profile data set includes a measured force or pressure as a function of displacement of an actuator effecting the cold-working material-processing operation that includes cold expansion of a hole at the target portion of the workpiece to impart residual stress to the workpiece (Ross, par [0029] discloses a processing tool with a drive member and actuator to provide pressure at a position to obtain a level of cold working of a hole in a workpiece.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine training a neural network regarding a process performed on a workpiece teaching of Liu with training a neural network using data of a workpiece after a cold expansion process teaching of Toktas and the teaching of a controller controlling a tool to perform cold work on a workpiece in Ross. The motivation to do so would have been because Ross discloses the benefit of allowing processing validation of a hole-by-hole or a workpiece-by-workpiece basis, while also providing insights into the process and provide real-time feedback during an analysis of response relationships (Ross, Abstract, lines 4 - 13).
As per claim 14, note the rejections of claim 2 above. The instant claim 14 recite substantially the same limitations as the above rejected claim 2 and are therefore rejected under the same prior art teachings.
For claim 4: The combination of Liu, Toktas, and Ross discloses claim 4: The method of claim 1, wherein
the input layer of the first deep feature-recognition network engine is communicatively coupled to a controller of a material-processing tool constructed to effect the cold-working material processing operation (Ross, par [0041] discloses a controller that controls the processing tools to perform cold work of a workpiece.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine training a neural network regarding a process performed on a workpiece teaching of Liu with training a neural network using data of a workpiece after a cold expansion process teaching of Toktas and the teaching of a controller controlling a tool to perform cold work on a workpiece and a controller to control the processing tool for cold work processing teaching of Ross. The motivation to do so would have been because Ross discloses the benefit of allowing processing validation of a hole-by-hole or a workpiece-by-workpiece basis, while also providing insights into the process and provide real-time feedback during an analysis of response relationships (Ross, Abstract, lines 4 - 13).
For claim 5: The combination of Liu, Toktas, and Ross discloses claim 5: The system of claim 4, wherein
the material-processing tool includes a drive member (Ross, par [0022] discloses a drive member of the processing tool.)
and at least one sensor arranged to produce a sensed output based on sensing of at least one of a pressure, a position of the drive member, a distance of travel of the drive member, or a reaction force resulting from a force applied directly or indirectly by the cold-working material processing operation (Ross, par [0027] discloses at least one sensor to sense pressure, location of a drive member, its distance, and reaction force from an applied force.)
wherein the controller of the material-processing tool is operative to produce the work-profile data set based on operation of the sensed output (Ross, par [0176] discloses a controller obtaining information and sending or transmitting output information, including material or characteristics and any associated events.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine training a neural network regarding a process performed on a workpiece teaching of Liu with training a neural network using data of a workpiece after a cold expansion process teaching of Toktas and the teaching of a controller controlling a tool to perform cold work on a workpiece, and additional teaching of a drive member and sensor working with a controller, also found in Ross. The motivation to do so would have been because Ross discloses the benefit of allowing processing validation of a hole-by-hole or a workpiece-by-workpiece basis, while also providing insights into the process and provide real-time feedback during an analysis of response relationships (Ross, Abstract, lines 4 - 13).
Allowable Subject Matter
Claims 3, 8 - 10, and 15 are dependent upon a rejected base claim under 35 U.S.C. 103, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art of Nigrelli et al. (“Effects of Cold Working of Holes on Fatigue Crack Growth - Part I - Experimental Activity”) discloses measurements of an aluminum allow plate, including holes and sleeves, for testing including cold worked specimens, a decrease of velocity of crack propagation due to cold working, a lack of temperature values influenced by cold working, and curve fitting for edges of holes regarding the cold worked specimen, Ross et al. (U.S. PG Pub 2013/0200543 A1) discloses coldworking or expanding of a hole regarding a workpiece, reaction force, Ritchey et al. (U.S. PG Pub 2016/0069813 A1) discloses a machining operation and forming a feature on a workpiece, and determining if monitored parameters are within range limits, Liu et al (“Deep Learning in Sheet Metal Bending with a Novel Theory-Guided Deep Neural Network”) discloses using neural network and computer-aided design in producing a desired workpiece, including training a neural network in FIG. 3, and discloses a use of artificial neural network simulation modeling to simulate stress associated with an expanded hole , validating different methods to provide similarities in residual stress distributions, and Toktas et al. (“Artificial Neural Networks Solution to Display Residual Hoop Stress Field Encircling a Split-Sleeve Cold Expanded Aircraft Fastener Hole”) discloses training a neural network using data of a workpiece after a cold expansion process.
However, none of the references cited, including the prior art of Liu, Toktas and Ross, taken either alone or in combination with the prior art of record discloses:
Claims 3 and 15, using forward-propagating process in a training neural network engine to produce test results from inputs regarding work-profile data sets and labeled items representing preview material-processing operation instances, with adjustable parameters refined and applied to the forward-propagating process to reduce a difference between results of testing and ground truth information regarding the labeled items, and using the refined adjustable parameters for tuning the first deep feature-recognition network engine, in combination with the remaining elements and features of the claimed invention with regards to providing an assessment of the quality of the cold working process. It is for these reasons that the applicants’ invention defines over the prior art of record.
Claim 8, in which the first deep feature-recognition network engine includes a linear layer in the form of an input layer to provide a first layer output to a convolution and activation layer with a second layer output sending information to a pooling layer, with a third layer output sending data to a fully-connected and activation layer with a fourth layer sending data to a fully connected and probability layer with the output, in combination with the remaining elements and features of the claimed invention with regards to providing an assessment of the quality of the cold working process. It is for these reasons that the applicants’ invention defines over the prior art of record.
Dependent claims 9 and 10 are allowable under 35 U.S.C. 103 for depending from claim 8, an allowable base claim under 35 U.S.C. 103.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 12: The prior art of Nigrelli et al. (“Effects of Cold Working of Holes on Fatigue Crack Growth - Part I - Experimental Activity”) discloses measurements of an aluminum allow plate, including holes and sleeves, for testing including cold worked specimens, a decrease of velocity of crack propagation due to cold working, a lack of temperature values influenced by cold working, and curve fitting for edges of holes regarding the cold worked specimen, Ross et al. (U.S. PG Pub 2013/0200543 A1) discloses coldworking or expanding of a hole regarding a workpiece, reaction force, Ritchey et al. (U.S. PG Pub 2016/0069813 A1) discloses a machining operation and forming a feature on a workpiece, and determining if monitored parameters are within range limits, Liu et al (“Deep Learning in Sheet Metal Bending with a Novel Theory-Guided Deep Neural Network”) discloses using neural network and computer-aided design in producing a desired workpiece, including training a neural network in FIG. 3, and discloses a use of artificial neural network simulation modeling to simulate stress associated with an expanded hole , validating different methods to provide similarities in residual stress distributions, and Toktas et al. (“Artificial Neural Networks Solution to Display Residual Hoop Stress Field Encircling a Split-Sleeve Cold Expanded Aircraft Fastener Hole”) discloses training a neural network using data of a workpiece after a cold expansion process.
However, none of the references cited, including the prior art of Liu, Toktas and Ross, taken either alone or in combination with the prior art of record discloses for claim 12: A method for training a deep feature-recognition network for use with material-processing operations, including the steps of a test result obtained based on using adjustable parameters refined and applied to the forward-propagating process to reduce a difference between results of testing and ground truth information regarding the labeled items, and using the refined adjustable parameters for tuning the first deep feature-recognition network engine, in combination with the remaining elements and features of the claimed invention with regards to providing an assessment of the quality of the cold working process. It is for these reasons that the applicants’ invention defines over the prior art of record.
Claim 18: The prior art of Nigrelli et al. (“Effects of Cold Working of Holes on Fatigue Crack Growth - Part I - Experimental Activity”) discloses measurements of an aluminum allow plate, including holes and sleeves, for testing including cold worked specimens, a decrease of velocity of crack propagation due to cold working, a lack of temperature values influenced by cold working, and curve fitting for edges of holes regarding the cold worked specimen, Ross et al. (U.S. PG Pub 2013/0200543 A1) discloses coldworking or expanding of a hole regarding a workpiece, reaction force, Ritchey et al. (U.S. PG Pub 2016/0069813 A1) discloses a machining operation and forming a feature on a workpiece, and determining if monitored parameters are within range limits, Liu et al (“Deep Learning in Sheet Metal Bending with a Novel Theory-Guided Deep Neural Network”) discloses using neural network and computer-aided design in producing a desired workpiece, including training a neural network in FIG. 3, and discloses a use of artificial neural network simulation modeling to simulate stress associated with an expanded hole , validating different methods to provide similarities in residual stress distributions, and Toktas et al. (“Artificial Neural Networks Solution to Display Residual Hoop Stress Field Encircling a Split-Sleeve Cold Expanded Aircraft Fastener Hole”) discloses training a neural network using data of a workpiece after a cold expansion process.
However, none of the references cited, including the prior art of Liu, Toktas and Ross, taken either alone or in combination with the prior art of record discloses for claim 18: A method for automated estimation of fatigue life of a workpiece after material-processing operations, including the steps of using both data regarding residual stress in the form of a profile as well as data regarding applied stress in the form of a profile, to obtain activations using profiles to produce results approximating the fatigue life of a workpiece after cold-working material-processing has been performed, in combination with the remaining elements and features of the claimed invention with regards to providing an assessment of the quality of the cold working process. It is for these reasons that the applicants’ invention defines over the prior art of record.
Dependent claims 19 - 22 are allowable under 35 U.S.C. 103 for depending from claim 18, an allowable base claim under 35 U.S.C. 103.
Claim 23: The prior art of Nigrelli et al. (“Effects of Cold Working of Holes on Fatigue Crack Growth - Part I - Experimental Activity”) discloses measurements of an aluminum allow plate, including holes and sleeves, for testing including cold worked specimens, a decrease of velocity of crack propagation due to cold working, a lack of temperature values influenced by cold working, and curve fitting for edges of holes regarding the cold worked specimen, Ross et al. (U.S. PG Pub 2013/0200543 A1) discloses coldworking or expanding of a hole regarding a workpiece, reaction force, Ritchey et al. (U.S. PG Pub 2016/0069813 A1) discloses a machining operation and forming a feature on a workpiece, and determining if monitored parameters are within range limits, Liu et al (“Deep Learning in Sheet Metal Bending with a Novel Theory-Guided Deep Neural Network”) discloses using neural network and computer-aided design in producing a desired workpiece, including training a neural network in FIG. 3, and discloses a use of artificial neural network simulation modeling to simulate stress associated with an expanded hole , validating different methods to provide similarities in residual stress distributions, and Toktas et al. (“Artificial Neural Networks Solution to Display Residual Hoop Stress Field Encircling a Split-Sleeve Cold Expanded Aircraft Fastener Hole”) discloses training a neural network using data of a workpiece after a cold expansion process.
However, none of the references cited, including the prior art of Liu, Toktas and Ross, taken either alone or in combination with the prior art of record discloses for claim 23: An automated method for automated assessment of material-processing operations, including the steps of using curve-fitting parameters from values from a work-profile data set to fit a work profile data set to a reference curve, and using curve-fit-parameter decision criteria and curve-fitting parameter values to obtain a sequence of decisions for an output indicating how well the cold-working material-procession operation performed, in combination with the remaining elements and features of the claimed invention with regards to providing an assessment of the quality of the cold working process. It is for these reasons that the applicants’ invention defines over the prior art of record.
Dependent claims 24 - 28 are allowable under 35 U.S.C. 103 for depending from claim 23, an allowable base claim under 35 U.S.C. 103.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CEDRIC D JOHNSON whose telephone number is (571)270-7089. The examiner can normally be reached M-Th 4:30am - 2:00pm, F 4:30am - 11:30am.
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, Rehana Perveen can be reached at 571-272-3676. 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.
/Cedric Johnson/ Primary Examiner, Art Unit 2189
October 18, 2025