Detailed Action
Amendment
1. This office action is in response to applicant’s amendments dated 5-22-26 and this office action is a final rejection.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
2. 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.
Regarding claim 1, applicant has invoked 35 U.S.C. 112(f) means plus function analysis with respect to the claimed edge computing device and as seen in applicant’s originally filed disclosure the edge computing device is detailed as an edge computing device, including: at least one processor and a non-transitory computer-readable medium as seen in paragraph [0008] of applicant’s originally filed specification, is further detailed as a local, high power computing device, also known as an “edge computing device” as seen in paragraph [0037] of applicant’s originally filed specification, and is further detailed as a local, high bandwidth computer such as an edge computing device as detailed in paragraph [0102] of applicant’s originally filed specification.
Claim Rejections - 35 USC § 112
3. 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 and 3-19 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.
Claim 1 is 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. Applicant has invoked 35 U.S.C. 112(f) means plus function analysis with respect to the claimed computing device as detailed earlier in paragraph 3 of this office action and the terms/phrases of applicant’s originally filed specification being, “for instance” in paragraph [0067], “in some examples”, “any suitable” and “including but not limited to” in paragraph [0086], “in some examples” and “including but not limited to” in paragraph [0087], “including but not limited to” in paragraph [0088], “any suitable” and “any other suitable” in paragraph [0090], “e.g.” in paragraph [0099], “such as” in paragraph [0101], “such as”, “e.g.” and “etc.” in paragraph [0102], “such as” in paragraph [0103], “in some examples” and “in other examples” in paragraph [0104], “such as”, “e.g.” and “etc.” in paragraph [0106], “and specifically” and “such as” in paragraph [0107], and “for instance” in paragraph [0108], render the claim indefinite in that it is unclear to whether other types of computing devices then those disclosed are being contemplated by the claim. Further, the list of indefinite language in applicant’s specification detailed earlier provides examples of indefinite language and may not encompass all indefinite language related to the claimed computing device.
Claim Rejections - 35 USC § 103
4. 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.
Claim(s) 1 and 3-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2022/0026269 to Villerup et al. in view of U.S. Patent Application Publication No. 2021/0204553 to Mehta et al.
Referring to claim 1, Villerup et al. discloses a computer-implemented method of optimizing machine processing of a workpiece, the method comprising, receiving, by a computing device – at 1108, at least one sensor input regarding a workpiece – see input from imaging sensors – at 1104,1105 in figures 11-12 and see paragraphs [0043]-[0044] and [0072]-[0073], performing, by the computing device – at 1108, pre-processing of the at least one sensor input – via 1106,1107, to transform the at least one sensor input into a pre-processed sensor data – see paragraphs [0043]-[0044] and [0072]-[0073], for at least use in one or more machine learning models – at 1107, executing, by a computing device – at 1108, one or more machine learning models – at 1107, to output requested information – at 1109,1209, regarding the workpiece based on data in the at least one sensor input – see figures 11-12 and paragraphs [0042]-[0044] and [0072]-[0073], receiving and processing, by the computing device – at 1108, the output – at 1109,1209, and controlling at least one aspect of the machine processing of the workpiece – see via 1211, by the computing device – at 1108, in response to the output – at 1109,1209 processed by the computing device – at 1108 – see figures 11-12 and paragraphs [0072]-[0073]. Villerup et al. further discloses execution of the one or more machine learning models – at 1107, is carried out by the computing device – at 1108, and wherein controlling at least one aspect of the machine processing of the workpiece – at 1101,1211,1212, in response to the processed output is carried out by a machine computer – at 1108, of a workpiece processing system – at 1211, configured to carry out at least one aspect of processing the workpiece – at 1101,1212 – see figures 11-12 and paragraphs [0073]-[0074]. Villerup et al. does not disclose the learning models are carried out by an edge computing device. However, it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and use any suitable computing device for carrying out the learning models including the edge computing device, so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired. Villerup et al. further does not disclose pre-processing of the at least one sensor input, for at least one of efficient transfer to an edge computing device, receiving by the edge computing device the pre-processed sensor data and executing the language models via the edge computing device. Mehta et al. does disclose pre-processing of the at least one sensor input – from 116, for at least one of efficient transfer from the computing device – at 132, to an edge computing device – at 134, receiving by the edge computing device – at 134,920,930,932, the pre-processed sensor data – see figures 1a-4a and paragraphs [0032]-[0036] and [0040]-[0048], and executing the language models via the edge computing device – see figures 1a-4a and paragraphs [0032]-[0036] and [0040]-[0048]. Therefore it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and add the edge computing device for executing models based on the sensed information of the workpiece as disclosed by Mehta et al., so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired. Regarding the 35 U.S.C. 112(f) analysis of the computing device the computers – at 1108 of Villerup et al. and – at 132 of Mehta et al. would at least be functional equivalents of applicant’s disclosed computing device in that the computers of Villerup et al. and Mehta et al. would have a processor and memory and be consistent with applicant’s originally filed disclosure. Regarding the 35 U.S.C. 112(f) analysis of the edge computing device, the computer – at 134 of Mehta et al. would at least be a functional equivalent of applicant’s disclosed edge computing device in that the edge computer of Mehta et al. would have a processor and memory and be consistent with applicant’s originally filed disclosure.
Referring to claim 3, Villerup et al. as modified by Mehta et al. further discloses further discloses verifying, by the computing device – at 1108 of Villerup et al., the machine learning model output – at 1107,1109 of Villerup et al., from the edge computing device – at 134 of Mehta et al., from the computing device – at 132 of Mehta et al., corresponds to the at least one sensor input – at 1104,1105 – see figures 11-12 and paragraphs [0043]-[0044] and [0072]-[0073] of Villerup et al. and – see figures 1a-4a and paragraphs [0032]-[0036] and [0040]-[0048] of Mehta et al., and identifying, by the computing device – at 1108 of Villerup et al., a machine learning model – at 1107 of Villerup et al., included in the one or more machine learning models, with a unique identifier in a communication of the sensor input – at 1104,1105 – see weight and density identifiers detailed in paragraphs [0043]-[0044] and [0072]-[0073] of Villerup et al., between the computing device – at 132 and the edge computing device – at 134 – see figures 1a-4a and paragraphs [0032]-[0036] and [0040]-[0048] of Mehta et al. Therefore it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and add the edge computing device for executing models based on the sensed information of the workpiece as disclosed by Mehta et al., so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 4, Villerup et al. as modified by Mehta et al. further discloses the at least one sensor input corresponds to at least one image of the workpiece – see via 1104, the one or more machine learning models – at 1107, after receiving the pre-processed sensor data, are configured to perform at least one of, generating at least one of workpiece classification and a classification probability score for at least one possible type of workpiece for the workpiece, generating a region of interest in an image of the workpiece – region of interest being where to cut the workpiece as seen at 1211,1212 in figure 12 and paragraph [0073] of Villerup et al., or generating an outline in an image of the workpiece of at least one object or feature of the workpiece – not required by the claim given the “at least one of” and “or” clauses in the claim.
Referring to claim 5, Villerup et al. as modified by Mehta et al. further discloses the one or more machine learning models include a workpiece classification machine learning model – see for example paragraphs [0006], [0026], [0045]-[0047] and [0062] of Mehta et al., and wherein the workpiece classification machine learning model includes a convolutional neural network – see figures 4a-4b and paragraphs [0006], [0026], [0045]-[0047] and [0062] of Mehta et al. Therefore it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and add the classification learning model and neural network of Mehta et al., so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 6, Villerup et al. as modified by Mehta et al. further discloses the one or more machine learning models include an image segmentation machine learning model configured to identify features of the workpiece – see for example paragraphs [0045]-[0047] of Mehta et al., and wherein the image segmentation machine learning model includes a fully convolutional network – see for example paragraphs [0045]-[0047] of Mehta et al. Therefore it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and add the image segmentation model and neural network of Mehta et al., so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 7, Villerup et al. as modified by Mehta et al. further discloses generating, with the computing device – at 920,930,932 in figure 9 of Mehta et al., at least first and second binary masks that correspond to at least first and second features of the workpiece within a single input image including in the at least one sensor input – see features such as category of meat, cut of meat, non-meat tissue detailed in paragraphs [0045]-[0047] of Mehta et al., executing, with the computing device – at 920,930,932, a mask combiner engine to combine the at least first and second binary masks into a single multi-class mask – see figures 4a-4b and paragraphs [0045]-[0047] of Mehta et al., and training the image segmentation machine learning model, with the computing device – at 920,930,932, using the single multi-class mask – see figures 4a-4b and paragraphs [0045]-[0050]. Therefore it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and add the image segmentation model and neural network of Mehta et al., so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 8, Villerup et al. as modified by Mehta et al. further discloses receiving, with the computing device – at 132, images of first and second opposing surfaces of the workpiece – at 104,204, - see via items 116-136 in figures 1a-2b of Mehta et al., executing, with the edge computing device – at 134, an image segmentation machine learning model to generate a first output including an outline in an image of the first surface of the workpiece of at least one object or feature of the workpiece – see figures 4a-4b and paragraphs [0045]-[0047] of Mehta et al., executing, with the edge computing device – at 134, an image segmentation machine learning model to generate a second output including an outline in an image of the second surface of the workpiece of at least one object or feature of the workpiece – see figures 4a-4b and paragraphs [0045]-[0047] of Mehta et al., correlating, with the edge computing device – at 134, the at least one object or feature outlined in the image of the first surface of the workpiece with the at least one object or feature outlined in the image of the second surface of the workpiece – see figures 4a-4b and paragraphs [0045]-[0047] of Mehta et al., generating, with the edge computing device – at 134, a 3D model of the workpiece using the first and second outputs – see figures 4a-4b and paragraphs [0045]-[0047] of Mehta et al., the 3D model showing correlated at least one objects or features extending between the first and second opposing surfaces of the workpiece – see figures 4a-4b and paragraphs [0045]-[0047] of Mehta et al. Therefore it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and add the image segmentation model and neural network of Mehta et al., so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 9, Villerup et al. as modified by Mehta et al. further discloses generating, by the edge computing device – at 134, a 3D model of the workpiece using the first and second outputs includes, assigning X-Y coordinates to outlines of the least one objects or features extending between the first and second opposing surfaces of the workpiece – see figures 1a-4b and paragraphs [0031]-[0047] of Mehta et al., aligning the outlines of the least one objects or features extending between the first and second opposing surfaces of the workpiece – see figures 4a-4b and paragraphs [0031]-[0047] of Mehta et al., and at least one of, extrapolating the least one objects or features extending between the first and second opposing surfaces of the workpiece through a thickness of the workpiece, or extrapolating density data from the first surface of the workpiece down to the second opposing surface of the workpiece to estimate a shape of the second surface including any voids – see figures 11-12 and paragraphs [0043]-[0044] and [0072]-[0073] of Villerup et al. Therefore it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and add the image segmentation model and neural network of Mehta et al., so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 10, Villerup et al. as modified by Mehta et al. further discloses the image of the first surface of the workpiece is represented by an image of a surface of the workpiece – see figures 1a-4b and paragraphs [0045]-[0047] of Mehta et al., but does not disclose the image of the second surface of the workpiece is an image of a top surface of a prior cut workpiece. However, it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. as modified by Mehta et al. and have the image based on a top surface of a prior workpiece as claimed, so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 11, Villerup et al. as modified by Mehta et al. further discloses defining for a workpiece processing system – at 1211, with the computing device – at 1108, cut paths of the workpiece based on the least one objects or features – see figures 11-12 and paragraphs [0043]-[0044] and [0072]-[0073] of Villerup et al., identified in the 3D model – see figures 1a-4b and paragraphs [0031]-[0047] of Mehta et al. Therefore it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and add the image segmentation model and neural network of Mehta et al., so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 12, Villerup et al. as modified by Mehta et al. further discloses executing, with the edge computing device – at 134, a workpiece classification machine learning model to generate at least one of a workpiece classification – see classification detailed in paragraphs [0042]-[0050] of Mehta et al., and a classification probability score of at least one possible type of workpiece for the workpiece as output using the 3D model of the workpiece as input – see figures 1-4b and paragraphs [0042]-[0050] of Mehta et al. Therefore it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and add the image segmentation model and neural network of Mehta et al., so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 13, Villerup et al. as modified by Mehta et al. further discloses receiving and processing, by the computing device – at 132,920,930,932, the output of a classification probability score from the edge computing device – at 134, corresponding the output of the workpiece classification machine learning model, the processing of the classification probability score includes categorizing the workpiece based on at least one of first and second classification probability scores for the workpiece – see figures 1-4b and paragraphs [0042]-[0050] of Mehta et al., using a demand for a first type of workpiece corresponding to the first classification probability score and a demand for a second type of workpiece corresponding to the second classification probability score – see paragraphs [0049]-[0050] of Mehta et al. Therefore it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and add the image segmentation model and neural network of Mehta et al., so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 14, Villerup et al. as modified by Mehta et al. further discloses generating, with the edge computing device – at 134, the classification probability score of the at least one possible type of workpiece for the workpiece includes at least one of: providing a label for the at least one possible type of workpiece if the classification probability score exceeds a minimum threshold – see figures 1-5 and paragraphs [0042]-[0050] of Mehta et al., providing a list of first and second possible types of workpieces for the workpiece based on a first and second highest classification probability scores included in the classification probability score – see paragraphs [0042]-[0050] of Mehta et al., or providing a list of possible types of workpieces for the workpiece and corresponding classification probability scores for each type – see figures 1-5 and paragraphs [0042]-[0050] of Mehta et al. Therefore it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and add the image segmentation model and neural network of Mehta et al., so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 15, Villerup et al. as modified by Mehta et al. further discloses receiving and processing, by the computing device – at 132,920,930,932, the output including a classification probability score of the at least one possible type of workpiece for the workpiece and categorizing the workpiece based on the classification probability score – see figures 1-5 and paragraphs [0042]-[0050] of Mehta, and a demand for the at least one possible type of workpiece, and performing, with a workpiece processing system coupled to the computing device – at 132,920,930,932, at least one of cutting, portioning, trimming, sorting, or packaging the workpiece based on the categorizing of the workpiece – see cutting in figure 12 of Villerup et al. and – see sorting in paragraphs [0042]-[0050] of Mehta et al. Therefore it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and add the image segmentation model and neural network of Mehta et al., so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 16, Villerup et al. as modified by Mehta et al. further discloses the generating the region of interest in the image of the workpiece includes at least one of: superimposing a substantially largest inscribing circle on the image of the workpiece in a fatty region of a steak likely to include a sciatic nerve – not required by the claim given the “at least one of” phrase, or superimposing an outline on the image of the workpiece defining a likely peak height portion of the workpiece – see figures 1-4b and paragraphs [0031]-[0050]. Therefore it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. and add the image segmentation model and neural network of Mehta et al., so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 17, Villerup et al. as modified by Mehta et al. further discloses the one or more machine learning models configured to generate the region of interest in the image of the workpiece by superimposing the substantially largest inscribing circle on the image of the workpiece in the fatty region of the steak likely to include the sciatic nerve are trained to manage class imbalance by at least one of: weighting a positive class representing a fatty region of a steak likely to include a sciatic nerve, more than a negative class representing regions other than the fatty region of the steak likely to include the sciatic nerve, and penalizing the model when it misses the positive class; oversampling images with the positive class; or under sampling images that do not contain the positive class – not required by the claim given the “at least one of” phrase in parent claim 16.
Referring to claim 18, Villerup et al. as modified by Mehta et al. does not disclose the one or more machine learning models configured to generate the region of interest in the image of the workpiece include an EfficientNet (ENet) semantic binary segmentation model. However, it would have been obvious to one of ordinary skill in the art to take the method of Villerup et al. as modified by Mehta et al. and use any suitable learning model including the EfficientNet model claimed, so as to yield the predictable result of more quickly and accurately automatically processing the workpiece as desired.
Referring to claim 19, Villerup et al. as modified by Mehta et al. further discloses the generating the outline in the image of the workpiece of the at least one object or feature of the workpiece includes at least one of, outlining at least one of a bone, a fat/lean boundary, an edge of the workpiece, a perimeter of the workpiece, a bottom surface of the workpiece, or cut lines of the workpiece – see edge, perimeter and cut lines as seen in figures 11-12 and paragraphs [0043]-[0044] and [0072]-[0073] of Villerup et al., or outputting a multi-class output image including outlines of at least two types of features included in the at least one object or feature of the workpiece– see weight and density features in figures 11-13 and paragraphs [0043]-[0044] and [0072]-[0075] of Villerup et al.
Response to Arguments
5. Applicant’s claim amendments and remarks/arguments dated 5-22-26 obviates the 35 U.S.C. 101 rejections of claims 1 and 3 detailed in the last office action dated 2-23-26.
Applicant’s claim amendments and remarks/arguments obviates all of the 35 U.S.C. 112(b) rejections of claims 1-4 and 7-17 detailed in the last office action dated 2-23-26 except for the rejection of claim 1 based on the 35 U.S.C. 112(f) analysis with respect to the claimed computing device. Regarding the 35 U.S.C. 112(f) analysis of the claimed computing device applicant has invoked this analysis in that the term “device” is considered the generic term similar to the term means (see MPEP section 2181), it has associated functional limitations being the claimed pre-processing of the sensor input and the claim does not disclose any particular structure related to the claimed computing device. Therefore as seen in applicant’s originally filed disclosure as detailed in paragraph 3 of this office action, there are instances of indefinite language as related to the disclosed computing device. It is recommended that applicant change computing device to computer in the claims.
Regarding the prior art rejections of claim 1, applicant’s claim amendments and remarks/arguments dated 5-22-26 obviates the prior art rejections detailed in the last office action dated 2-23-26. However, applicant’s claim amendments dated 5-22-26 necessitates the new grounds of rejection detailed earlier in paragraph 4 of this office action. Further, the Villerup et al. reference US 2022/0026269 discloses receiving, by a computing device – at 1108, at least one sensor input regarding a workpiece – see input from imaging sensors – at 1104,1105 in figures 11-12 and see paragraphs [0043]-[0044] and [0072]-[0073], performing, by the computing device – at 1108, pre-processing of the at least one sensor input – via 1106,1107, to transform the at least one sensor input into a pre-processed sensor data – see paragraphs [0043]-[0044] and [0072]-[0073], for at least use in one or more machine learning models – at 1107, executing, by a computing device – at 1108, one or more machine learning models – at 1107, to output requested information – at 1109,1209, regarding the workpiece based on data in the at least one sensor input – see figures 11-12 and paragraphs [0042]-[0044] and [0072]-[0073], receiving and processing, by the computing device – at 1108, the output – at 1109,1209, and controlling at least one aspect of the machine processing of the workpiece – see via 1211, by the computing device – at 1108, in response to the output – at 1109,1209 processed by the computing device – at 1108 – see figures 11-12 and paragraphs [0072]-[0073]. Adding another computer such as the edge computer of Mehta et al. US 2021/0204553 to the method of Villerup et al. would not render the method of Villerup et al. inoperable in that Villerup et al. discloses a computer based electrical control to perform the method and the combination of these references renders the claims obvious as detailed earlier in paragraph 4 of this office action.
Regarding the prior art rejections of claims 3-19, applicant relies upon the same arguments with respect to parent claim 1 discussed earlier.
Conclusion
6. 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.
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID J PARSLEY whose telephone number is (571)272-6890. The examiner can normally be reached Monday-Friday, 8am-4pm EST.
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/DAVID J PARSLEY/Primary Examiner, Art Unit 3643
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