CTNF 18/763,388 CTNF 83064 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA 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. 07-30-05 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. 07-30-06 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: “acquirer that acquires”, “processing section that generates”, “image inspector that inspects”, “first acquirer that acquires”, “second acquirer that acquires” and “marking section that applies” in claims 1, 13, 15, 17 and 19. 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 § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-4 and 8-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. This analysis is based on the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (2024 AI SME Update) published on July 17, 2024 (89 FR 58128). Step 1 : Claims 1-4 and 8-23 are directed to a system, method and recording medium which fall under the statutory categories of invention of methods and machines. Therefore, step 1 is met. Step 2A, Prong 1 : Claims 1, 22 and 23 recite “inspecting the bundle based on the acquired information” The limitations, excluding the processor, therefore fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Under its broadest reasonable interpretation when read in light of the specification, the use of one or more neural networks encompasses mental processes practically performed in the human mind. See MPEP 2106.04(a)(2), subsection III. Dependent claims 2-4, 15, 20 and 21 further clarify previously established limitations that may be practically performed in the human mind using observation, evaluation, judgement, and opinion. For example, a person could look at an image defect report of the pages to determine whether the bundle deviates from the expected values by observing and deciding whether the deviation is large enough. When a person determines there is a significant defect in the bundle, they could note such and subsequently cease additional printing in order to remedy the situation. Step 2A, Prong 2 : The limitations of claims 1 and 23 are recited as being performed by a “processor” or computer. The processor/computer is recited at a high level of generality. The processor/computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f), which provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. In evaluation of whether the invention integrates into a practical application, it should be clear that the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1) and 2106.05(a). According to the specification, the improvement is to automate a previously manual process of bound media inspection. This is stated as being accomplished by using a trained deep learning model that uses instances of basis weight and number of pages within a bundle as the training data. While this is properly reflected in dependent claims 5-7, which notably lack 101 rejections, this is not clearly demonstrated in claims 1-4 and 8-23. Step 2B : In claims 1, 22 and 23, the limitation of “acquiring information regarding a shape of the bundle including the plurality of recording media” amounts to merely receiving data. This limitation is considered to be insignificant extra-solution activity. In consideration, this limitation is further evaluated to take into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). Receiving data is very well understood and routine in the field and therefore these do not add an inventive concept to the claims. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim (s) 1, 13, 18, 19 and 21-23 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Blohm et al. (US 2004/0265095) . Regarding claim 1 , Blohm et al. discloses an inspection system that inspects a bundle including a plurality of processed recording media, the inspection system comprising: an acquirer that acquires information regarding a shape of the bundle including the plurality of processed recording media (“Each book is inspected at the station 18 by a caliper or other sensor to detect the thickness or print quality of the book” at paragraph 0023, line 5); and a hardware processor, wherein the hardware processor inspects the bundle based on the information acquired by the acquirer (“This information is transmitted to the controller 16, which compares the measured thickness or print quality with a reference thickness or print quality, in order to determine if the book has been appropriately assembled” at paragraph 0023, second to last sentence). Regarding claim 22 , Blohm et al. discloses an inspection method for inspecting a bundle including a plurality of processed recording media, the inspection method comprising: acquiring information regarding a shape of the bundle including the plurality of processed recording media (“Each book is inspected at the station 18 by a caliper or other sensor to detect the thickness or print quality of the book” at paragraph 0023, line 5); and inspecting the bundle based on the information acquired by the acquirer (“This information is transmitted to the controller 16, which compares the measured thickness or print quality with a reference thickness or print quality, in order to determine if the book has been appropriately assembled” at paragraph 0023, second to last sentence). Regarding claim 23 , Blohm et al. discloses a non-transitory recording medium storing a computer-readable inspection program for inspecting a bundle including a plurality of processed recording media, the inspection program causing a computer to execute: acquiring information regarding a shape of the bundle including the plurality of processed recording media (“Each book is inspected at the station 18 by a caliper or other sensor to detect the thickness or print quality of the book” at paragraph 0023, line 5); and inspecting the bundle based on the information acquired by the acquirer (“This information is transmitted to the controller 16, which compares the measured thickness or print quality with a reference thickness or print quality, in order to determine if the book has been appropriately assembled” at paragraph 0023, second to last sentence). Regarding claim 13 , Blohm et al. discloses system further comprising a processing section that generates a bundle including a plurality of recording media by processing, wherein the hardware processor inspects the bundle processed by the processing section (“The binding line 10 includes a controller 16, as is known in the art, that maintains a master mailing list having a prearranged sequence or order, such as, for example, a zip code order. The controller 16 controls the feeders 12 to assemble each book according to the mailing list information. The controller 16 monitors the position of each book as it is being assembled on the conveyor 14 and tracks the position of each book as it moves downstream of the feeders 12” at paragraph 0022). Regarding claim 18 , Blohm et al. discloses a system comprising an ejector that switches a conveyance destination of the bundle in accordance with a result of the inspection by the hardware processor (“If an error was made in the assembly of the book, the book is rejected from the binding line 10 at a divert gate 22” at paragraph 0023, last sentence). Regarding claim 19 , Blohm et al. discloses a system further comprising a marking section that applies marking to the bundle in accordance with a result of the inspection by the hardware processor (“Each acceptable book may be further personalized by printers 30 and 31, as illustrated at step 100” at paragraph 0032, second to last sentence). Regarding claim 21 , Blohm et al. discloses a system wherein, when the hardware processor determines that the bundle to be inspected is a defective product, the hardware processor records information regarding the defective product (“The recipients of the rejected books are identified in the mailing list and stored in the controller” at paragraph 0033, line 1) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 2-4, 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Blohm et al. and Zamir et al. (US 2023/0153979) . Regarding claim 2 , Blohm et al. discloses a system as described in claim 1 above. Blohm et al. does not explicitly disclose that the hardware processor inspects the bundle using a deep learning model. Zamir et al. teaches a system in the same field of endeavor of printed matter inspection, wherein the hardware processor inspects the printing using a deep learning model (“The image-based feature extractor may include unsupervised (e.g. unlabeled) training where the input and output are the same image, thereby obtaining a deep encoding network for extracting the image features” at paragraph 0039, second to last sentence). It would have been obvious to one ordinary skill in the art before the effective filing date of the invention to utilize a deep network as taught by Zamir et al. in the bundle defect evaluation of Blohm et al. as “usage during online inspection to rule-out ‘pure’ false-alarms (e.g., unambiguously attributed to differences unassociated with the actual defective material such as illumination and sensor noise). This allows the system to safely set classic-machine-vision-algorithms based inspection thresholds to ‘over-report’, thereby addressing issues of misdetection as well” (Zamir et al. at paragraph 0043, third to last sentence). Regarding claim 3 , Zamir et al. discloses a system wherein the hardware processor uses a model of an unsupervised learning method as the deep learning model (“The image-based feature extractor may include unsupervised (e.g. unlabeled) training where the input and output are the same image, thereby obtaining a deep encoding network for extracting the image features” at paragraph 0039, second to last sentence). Regarding claim 4 , Zamir et al. discloses a system wherein the hardware processor uses an autoencoder as the deep learning model (“As described above, bandwidth may be reduced by applying the feature-extraction block (e.g. based on convolutional-layers auto-encoder framework) on the edge (e.g. where the image data is being saved during online inspection), thus obtaining a compressed feature vector best preserving salient-for-detection/classification information that is passed to a server-entity to be concatenated with additional ‘contextual’ features for the auto-editing phase” at paragraph 0043, line 1). Regarding claim 12 , Blohm et al. discloses a system as described in claim 1 above. Blohm et al. does not explicitly disclose that the information acquired by the acquirer is two-dimensional information. Zamir et al. teaches a system in the same field of endeavor of printed matter inspection, wherein the information acquired by the acquirer is two-dimensional information (“Machine vision system 111 is configured to capture a plurality of quality control images 112 of instances 102a, 102b. Machine vision system may include an image capture device, such as a camera 110, disposed in a position (such as above an output of the production line 100” at paragraph 0033, line 1). It would have been obvious to one ordinary skill in the art before the effective filing date of the invention to utilize the machine vision system as taught by Zamir et al. in the bundle defect evaluation of Blohm et al. as “usage during online inspection to rule-out ‘pure’ false-alarms (e.g., unambiguously attributed to differences unassociated with the actual defective material such as illumination and sensor noise). This allows the system to safely set classic-machine-vision-algorithms based inspection thresholds to ‘over-report’, thereby addressing issues of misdetection as well” (Zamir et al. at paragraph 0043, third to last sentence). Regarding claim 20 , Blohm et al. discloses a system further comprising a system controller (controller 16 as described above is a system controller), wherein the processing includes stapling (“Downstream of the station 18, the binding line 10 includes a stitcher 24, where the books are bound, i.e., stapled, glued, stitched, and fastened” at paragraph 0024, line 1). Blohm et al. does not explicitly disclose that the system controller stops operation of the system when the hardware processor determines a defect related to the stapling. Zamir et al. teaches a system in the same field of endeavor of printed matter inspection, comprising a system controller wherein the system controller stops operation of the system when the hardware processor determines a defect (“In exemplary methods, the step of accepting at least some defect alerts (automatically or via the user interface) may prompt providing a feedback signal to a controller for the production line effective to stop the production line, to take an action on the defective product instances, or to otherwise impact the behavior of the production line” at paragraph 0047, line 4). It would have been obvious to one ordinary skill in the art before the effective filing date of the invention to utilize the machine vision system as taught by Zamir et al. in the bundle defect evaluation of Blohm et al. as “usage during online inspection to rule-out ‘pure’ false-alarms (e.g., unambiguously attributed to differences unassociated with the actual defective material such as illumination and sensor noise). This allows the system to safely set classic-machine-vision-algorithms based inspection thresholds to ‘over-report’, thereby addressing issues of misdetection as well” (Zamir et al. at paragraph 0043, third to last sentence) . 07-21-aia AIA Claim (s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Blohm et al. and Nishi (US 2022/0334520) . Blohm et al. discloses a system as described in claim 1 above. Blohm et al. does not explicitly disclose that the acquirer acquires the information using light emitted to the bundle. Nishi teaches a system in the same field of endeavor of printed matter defect detection, wherein the acquirer acquires the information using light emitted to the bundle (“The basis weight detector is a sensor that detects the basis weight of the paper sheet 90, includes a light emitter and a light receiver, and measures the basis weight by an attenuation amount of light transmitted through the paper sheet 90. For example, a basis weight sensor has the light emitter disposed below the paper sheet passing area (paper sheet passing path) into which the paper sheet is inserted, and the light receiver disposed above the paper sheet passing area. In this configuration, the paper sheet 90 passes between the light emitter and the light receiver, and the basis weight sensor detects the basis weight of the paper sheet 90 based on intensity of the light received by the light receiver to output the measurement result (measurement value 2)” at paragraph 0068). It would have been obvious to one ordinary skill in the art before the effective filing date of the invention to utilize the paper discrimination as taught by Nishi in the inspection model of Blohm et al. to enable the system to determine when the incorrect paper type is utilized in the printing . 07-21-aia AIA Claim (s) 8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Blohm et al. and Van Acquoij (US 2016/0103634) . Regarding claim 8 , Blohm et al. discloses a system as described in claim 1 above. Blohm et al. does not explicitly disclose that the information acquired by the acquirer is three-dimensional information. Van Acquoij teaches a system in the same field of endeavor of printed matter defect detection, wherein the information acquired by the acquirer is three-dimensional information (“A sensor device 22 in the form of an optical sensor, such as a laser scanner, is provided within the sentry unit 21 for sensing the surface geometry or topology of the sheets S as they travel on a first pass or a second pass along the transport path P. The laser scanner or optical sensor device 22 generates digital image data I of the three-dimensional surface geometry or topology of each sheet S sensed or scanned” at paragraph 0100, line 1). It would have been obvious to one ordinary skill in the art before the effective filing date of the invention to utilize the 3D sensor as taught by Van Acquoij in the system of Blohm et al. to be able to detect a variety of paper deformation types (see Van Acquoij at paragraph 0037). Regarding claim 11 , Van Acquoij discloses a system wherein the three-dimensional information is represented by a two-dimensional heat map or two-dimensional contour lines (“In a first processing step, a binary image is created where every pixel exceeding the preset height threshold given by TOL is set to 1, all other pixels are set to 0. The minimum threshold level for detecting defects is preferably set to 400 μm, as it has been found that a lower level will result in detection of too many very small defects. The processor device 24 produces a height map for each sheet. This height map is used to detect and measure or classify any defects present within the sheet, and particularly any out-of-plane deformations D, such as wrinkles, dog ears, curl, tears etc. In this embodiment, a defect is defined as a measurement point within the height map having at least 4 connected neighbors also exceeding a preset threshold value” at paragraph 0102) . 07-21-aia AIA Claim (s) 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Blohm et al. and Manabe (US 2022/0201150) . Regarding claim 14 , Blohm et al. discloses a system as described in claim 1 above. Blohm et al. does not explicitly disclose a printer that prints images on the recording media, wherein the plurality of recording media on which the images have been printed by the printer are processed by the processing section. Manabe teaches a system in the same field of endeavor of printed matter defect detection comprising a printer that prints images on the recording media (“The cut printed sheets are formed into sheet bundles in units of books by the buffer unit 0115. In the present embodiment, the buffer unit 0115 is connected in-line to a continuous feed printer postprocessor 0116 by a conveyance path. The image formed (printed) sheet bundles (paper bundles) in units of books are sent to the continuous feed printer postprocessor 0116, and are supplied as a target of processing by a continuous feed printer postprocessor 0116” at paragraph 0042, line 10), wherein the plurality of recording media on which the images have been printed by the printer are processed by the processing section (“The number of reference images to be read is the number of pages to he inspected (front side and back side of each sheet). In the inspection unit 0106, the conveyance of the sheet is detected by a sensor, for example, and the reference image is acquired by scanning the detected sheet” at paragraph 0084, line 1). It would have been obvious to one ordinary skill in the art before the effective filing date of the invention to utilize the system of Blohm et al. in direct conjunction with a printing workflow as taught by Manabe as “even if a defect in the print materials is found by the inspection apparatus 0108, it is converted into an inspection result (in units of books) and transmitted to the postprocessor control apparatus 0118, so that the image forming apparatus can continue printing without performing reprinting. Therefore, it is not necessary to prepare for reprinting or perform reprinting processing itself, and the productivity of the cutsheet device can be improved” (Manabe at paragraph 0146, line 2). Regarding claim 15 , Manabe discloses a system comprising an image inspector that inspects the images printed on the recording media by the printer, wherein the plurality of recording media on which the images have been inspected by the image inspector are processed by the processing section (“The image forming apparatus 0101 further includes an inspection unit 0106 and a large-capacity stacker 0107. The inspection unit 0106 is connected to the inspection apparatus 0108 via a cable 0114. A sheet on which an image has been formed by the image forming apparatus 0101 is read by the inspection unit 0106, and the quality of an image turned on the sheet is inspected by the inspection apparatus 0108” at paragraph 0041, line 1) . 07-21-aia AIA Claim (s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Blohm et al. and Nakano (US 2021/0374939) . Blohm et al. discloses a system as described in claim 1 above. Blohm et al. does not explicitly disclose that the acquirer includes a first acquirer that acquires information regarding a shape of the bundle on a front side of the bundle, and a second acquirer that acquires information regarding a shape of the bundle on a back side of the bundle. Nakano teaches a system in the same field of endeavor of printed matter defect detection wherein the acquirer includes a first acquirer that acquires information regarding a shape of the bundle on a front side of the bundle, and a second acquirer that acquires information regarding a shape of the bundle on a back side of the bundle (“The lower scanner 521 reads a lower surface of a printed matter (the sheet on which the printed image is formed by the image forming device 300) conveyed along the conveyance path. That is, the lower scanner 521 reads the printed image (formed on a front surface) of the printed matter that is back printed from a back surface side of the sheet through the sheet of the printed matter. The upper scanner 522 reads an upper surface of the printed matter conveyed along the conveyance path. That is, the upper scanner 522 reads the printed image of the printed matter that is back printed from a print surface side (front surface side) of the printed matter” at paragraph 0052, line 1). It would have been obvious to one ordinary skill in the art before the effective filing date of the invention to utilize the dual imaging as taught by Nakano on the bundle of Blohm et al. to ensure that the system is able to detect the visual condition of both sides of the bundle . 07-21-aia AIA Claim (s) 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Blohm et al. and Zamir et al. further in view of Nishi (US 2022/0334520) . Regarding claim 5 , the Blohm et al. and Zamir et al. combination discloses a system as described in claim 2 above. The Blohm et al. and Zamir et al. combination does not explicitly disclose that the deep learning model is a model trained for each basis weight of a cover sheet of the bundle, and when the hardware processor inspects the bundle using the deep learning model, the hardware processor refers to a model corresponding to the basis weight of the cover sheet of the bundle. Nishi teaches a system in the same field of endeavor of printed matter defect detection, wherein the learning model is a model trained for each basis weight of a cover sheet (“Furthermore, the image forming apparatus 10 performs paper type discrimination from the measurement result (measurement values 1 to 3) obtained from the medium detection apparatus 40. The storage 12 of the image forming apparatus 10 stores a learning model obtained by machine learning (also referred to as a trained model) used for paper type discrimination. This is a learning model generated by supervised learning using training data, with detection output of the medium detection apparatus 40 for the paper sheet 90 as an input value and paper type information of the paper sheet 90 set by the user as a correct label. The controller 11 of the image forming apparatus 10 obtains a basis weight conversion value, a paper thickness conversion value, and a surface property measurement value by using the measurement result (measurement values 1 to 3) obtained by measuring the paper sheet 90 by using the paper thickness detector, the basis weight detector, and the surface property detector of the medium detection apparatus 40 ” at paragraph 0072, line 1), and when the hardware processor inspects the printed matter using the learning model, the hardware processor refers to a model corresponding to the basis weight of the cover sheet (“Furthermore, the paper sheet information may be associated with the paper sheet feeding tray that uses a paper sheet physical property measured by the medium detection apparatus 40 and/or a paper type (paper sheet type) determined from the paper sheet physical property, and a basis weight, and this may be used as the paper information” at paragraph 0082, last sentence). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the paper discrimination as taught by Nishi in the inspection model of the Blohm et al. and Zamir et al. combination to enable the system to determine when the incorrect paper type is utilized in the printing. Regarding claim 6 , the Blohm et al. and Zamir et al. combination discloses a system as described in claim 1 above. The Blohm et al. and Zamir et al. combination does not explicitly disclose that the deep learning model is a model trained for each number of recording media forming the bundle, and when the hardware processor inspects the bundle using the deep learning model, the hardware processor refers to a model corresponding to the number of recording media forming the bundle. Nishi teaches a system in the same field of endeavor of printed matter defect detection, wherein the learning model is a model trained for the thickness of recording media (“Furthermore, the image forming apparatus 10 performs paper type discrimination from the measurement result (measurement values 1 to 3) obtained from the medium detection apparatus 40. The storage 12 of the image forming apparatus 10 stores a learning model obtained by machine learning (also referred to as a trained model) used for paper type discrimination. This is a learning model generated by supervised learning using training data, with detection output of the medium detection apparatus 40 for the paper sheet 90 as an input value and paper type information of the paper sheet 90 set by the user as a correct label. The controller 11 of the image forming apparatus 10 obtains a basis weight conversion value, a paper thickness conversion value, and a surface property measurement value by using the measurement result (measurement values 1 to 3) obtained by measuring the paper sheet 90 by using the paper thickness detector, the basis weight detector, and the surface property detector of the medium detection apparatus 40 ” at paragraph 0072, line 1), when the hardware processor inspects the printed matter using the learning model, the hardware processor refers to a model corresponding to the thickness of recording media (“Furthermore, the paper sheet information may be associated with the paper sheet feeding tray that uses a paper sheet physical property measured by the medium detection apparatus 40 and/or a paper type (paper sheet type) determined from the paper sheet physical property, and a basis weight, and this may be used as the paper information” at paragraph 0082, last sentence). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the paper discrimination as taught by Nishi in the inspection model of the Blohm et al. and Zamir et al. combination to enable the system to determine when the incorrect paper type is utilized in the printing. Therefore, the combined teachings of paper thickness modeling in addition to the disclosure of Blohm et al. evaluating the imaged bundle for the expected overall thickness, one may therefore derive the number of pages for the bundle given these two values. Regarding claim 7 , the Blohm et al. and Zamir et al. combination discloses a system as described in claim 1 above. The Blohm et al. and Zamir et al. combination does not explicitly disclose that the deep learning model is a model trained for each combination of the basis weight of the cover sheet of the bundle and the number of recording media forming the bundle, and when the hardware processor inspects the bundle using the deep learning model, the hardware processor refers to a model corresponding to the combination. Nishi teaches a system in the same field of endeavor of printed matter defect detection, wherein the learning model is a model trained for each combination of the basis weight of the cover sheet of the bundle and the thickness of recording media (“Furthermore, the image forming apparatus 10 performs paper type discrimination from the measurement result (measurement values 1 to 3) obtained from the medium detection apparatus 40. The storage 12 of the image forming apparatus 10 stores a learning model obtained by machine learning (also referred to as a trained model) used for paper type discrimination. This is a learning model generated by supervised learning using training data, with detection output of the medium detection apparatus 40 for the paper sheet 90 as an input value and paper type information of the paper sheet 90 set by the user as a correct label. The controller 11 of the image forming apparatus 10 obtains a basis weight conversion value, a paper thickness conversion value, and a surface property measurement value by using the measurement result (measurement values 1 to 3) obtained by measuring the paper sheet 90 by using the paper thickness detector, the basis weight detector, and the surface property detector of the medium detection apparatus 40 ” at paragraph 0072, line 1), and when the hardware processor inspects the printed using the learning model, the hardware processor refers to a model corresponding to the combination (“Furthermore, the paper sheet information may be associated with the paper sheet feeding tray that uses a paper sheet physical property measured by the medium detection apparatus 40 and/or a paper type (paper sheet type) determined from the paper sheet physical property, and a basis weight, and this may be used as the paper information” at paragraph 0082, last sentence). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the paper discrimination as taught by Nishi in the inspection model of the Blohm et al. and Zamir et al. combination to enable the system to determine when the incorrect paper type is utilized in the printing. Therefore, the combined teachings of paper thickness modeling in addition to the disclosure of Blohm et al. evaluating the imaged bundle for the expected overall thickness, one may therefore derive the number of pages for the bundle given these two values . 07-22-aia AIA Claim (s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Blohm et al. and Zamir et al . as applied to claim 2 above, and further in view of d'Armancourt et al. (US 10,834,283) . The Blohm et al. and Zamir et al. combination discloses a system as described in claim 2 above. The Blohm et al. and Zamir et al. combination does not explicitly disclose that the information acquired by the acquirer is three-dimensional information, the hardware processor normalizes the three-dimensional information according to a thickness and a size of the bundle, and the deep learning model uses data normalized by the hardware processor. d'Armancourt et al. teaches a system in the same field of endeavor of printed matter defect detection, wherein the information acquired by the acquirer is three-dimensional information (“The image may be captured by any method of digital image capture, such as imagers, image sensors (such as an integrated 1D, 2D, or 3D image sensor)” at col. 11, line 9), the hardware processor normalizes the three-dimensional information according to a thickness and a size of the bundle (“FIG. 4 shows a method 400 for image processing, according to an embodiment. At step 402, a size of a produced image is normalized to match a size of a reference image” at col. 15, line 3; “Normalizing a size of a produced image at step 402 can include adjusting a size of the produced image to align corners and/or edges of the produced image with corners and/or edges of the reference image. Additionally or alternatively, normalizing a size of a produced image at step 402 can include equalizing a distance between a rightmost printed area and a leftmost printed area of the image” at col. 15, line 25; though not explicit, the reference image has a particular size associated with the object of interest and in the case of Blohm et al., the expected thickness of the book is to be examined), and the learning model uses data normalized by the hardware processor (“And at step 406, a difference image is produced by comparing the normalized produced image to the reference image” at col. 15, line 7; “The database can be self-learning, and take into account the frequency of occurrence of each detected issue, to improve determination of the root cause and/or issue resolution in the future” at col. 29, line 2). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the normalization as taught by d'Armancourt et al. in the system of the Blohm et al. and Zamir et al. combination to ensure the image is processed according to a standardized size condition for fair evaluation of whether there is a defect present or not . 07-22-aia AIA Claim (s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Blohm et al. and Zamir et al . as applied to claim 2 above, and further in view of Van Acquoij . The Blohm et al. and Zamir et al. combination discloses a system as described in claim 2 above. The Blohm et al. and Zamir et al. combination does not explicitly disclose that the information acquired by the acquirer is three-dimensional information, and the hardware processor generates an image of the three-dimensional information, and data of the image generated by the hardware processor is used for the deep learning model. Van Acquoij teaches a system in the same field of endeavor of printed matter defect detection, wherein the information acquired by the acquirer is three-dimensional information (“A sensor device 22 in the form of an optical sensor, such as a laser scanner, is provided within the sentry unit 21 for sensing the surface geometry or topology of the sheets S as they travel on a first pass or a second pass along the transport path P. The laser scanner or optical sensor device 22 generates digital image data I of the three-dimensional surface geometry or topology of each sheet S sensed or scanned” at paragraph 0100, line 1), and the hardware processor generates an image of the three-dimensional information (“In a first processing step, a binary image is created where every pixel exceeding the preset height threshold given by TOL is set to 1, all other pixels are set to 0. The minimum threshold level for detecting defects is preferably set to 400 μm, as it has been found that a lower level will result in detection of too many very small defects. The processor device 24 produces a height map for each sheet. This height map is used to detect and measure or classify any defects present within the sheet, and particularly any out-of-plane deformations D, such as wrinkles, dog ears, curl, tears etc. In this embodiment, a defect is defined as a measurement point within the height map having at least 4 connected neighbors also exceeding a preset threshold value” at paragraph 0102), and data of the image generated by the hardware processor is used for the learning model (“the processing device might contain an auto-learning algorithm for improving its classification abilities during continued operation” at paragraph 0020, line 9). It would have been obvious to one ordinary skill in the art before the effective filing date of the invention to utilize the 3D sensor as taught by Van Acquoij in the system of the Blohm et al. and Zamir et al. combination to be able to detect a variety of paper deformation types (see Van Acquoij at paragraph 0037). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATRINA R FUJITA whose telephone number is (571)270-1574. The examiner can normally be reached Monday - Friday 9:30-5:30 pm ET. 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, Sumati Lefkowitz can be reached at 5712723638. 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. /KATRINA R FUJITA/Primary Examiner, Art Unit 2672 Application/Control Number: 18/763,388 Page 2 Art Unit: 2672 Application/Control Number: 18/763,388 Page 3 Art Unit: 2672 Application/Control Number: 18/763,388 Page 4 Art Unit: 2672 Application/Control Number: 18/763,388 Page 5 Art Unit: 2672 Application/Control Number: 18/763,388 Page 6 Art Unit: 2672 Application/Control Number: 18/763,388 Page 7 Art Unit: 2672 Application/Control Number: 18/763,388 Page 8 Art Unit: 2672 Application/Control Number: 18/763,388 Page 9 Art Unit: 2672 Application/Control Number: 18/763,388 Page 10 Art Unit: 2672 Application/Control Number: 18/763,388 Page 11 Art Unit: 2672 Application/Control Number: 18/763,388 Page 12 Art Unit: 2672 Application/Control Number: 18/763,388 Page 13 Art Unit: 2672 Application/Control Number: 18/763,388 Page 14 Art Unit: 2672 Application/Control Number: 18/763,388 Page 15 Art Unit: 2672 Application/Control Number: 18/763,388 Page 16 Art Unit: 2672 Application/Control Number: 18/763,388 Page 17 Art Unit: 2672 Application/Control Number: 18/763,388 Page 18 Art Unit: 2672 Application/Control Number: 18/763,388 Page 19 Art Unit: 2672 Application/Control Number: 18/763,388 Page 21 Art Unit: 2672 Application/Control Number: 18/763,388 Page 22 Art Unit: 2672 Application/Control Number: 18/763,388 Page 23 Art Unit: 2672 Application/Control Number: 18/763,388 Page 24 Art Unit: 2672 Application/Control Number: 18/763,388 Page 25 Art Unit: 2672 Application/Control Number: 18/763,388 Page 26 Art Unit: 2672 Application/Control Number: 18/763,388 Page 27 Art Unit: 2672 Application/Control Number: 18/763,388 Page 28 Art Unit: 2672 Application/Control Number: 18/763,388 Page 29 Art Unit: 2672
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