CTNF 18/429,632 CTNF 95621 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. Drawings The drawing is objected because graph in Figure 7 is not properly labeled. 06-22 No new matter should be entered. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. 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: 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 limitations are “a plurality of sensors of different types configured to”, and “a feature extractor module configured to” in claim 1; “a coarse detector configured … to” in claims 1 and 4; “a fine detector configured … to” in claims 1, 4-5, and 9; and “a control module configured to” in claims 1 and 7. The claims describe the various modules in functional terms of what they do, rather than how they do it. Under 35 USC 112(f), the Specification must identify a specific and readily-identifiable algorithm in the Specification associated with the claimed function. 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, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. For example, [0005] discloses “A defect detection system is disclosed and includes: sensors of different types configured to scan battery electrode material and generate output signals including respective image data; a feature extractor module configured to receive the output signals and perform feature extraction to fuse the image data of the output signals to generate feature maps for respective portions of the battery electrode material; a coarse detector configured, based on the feature maps, to determine whether there are defects in the portions of the battery electrode material, and generate binary information for each of the portions indicating whether the portions include one or more defects; and a fine detector configured, based on the binary information and at least one of the feature maps and image data, to classify the defects. A control module is configured to at least one of … ”. In order to exam the merit, Examiner interprets the above mentioned limitations performed by a generic computer. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. As to claim 1 , the claim recites “A defect detection system comprising: a plurality of sensors of different types configured to scan battery electrode material and generate a plurality of output signals including respective image data; a feature extractor module configured to receive the plurality of output signals and perform feature extraction to fuse the image data of the plurality of output signals to generate feature maps for respective portions of the battery electrode material ; a coarse detector configured, based on the feature maps , to determine whether there are defects in the portions of the battery electrode material , and generate binary information for each of the portions indicating whether the portions include one or more defects ; a fine detector configured, based on the binary information and at least one of the feature maps and image data, to classify the defects ; and a control module configured to at least one of i) report the classified defects, ii) determine whether one or more of the portions of the battery electrode material which have one or more defects ought to be discarded , iii) perform operations to discard the one or more portions of the battery electrode material which have one or more defects , and iv) perform feedback process control operations to prevent future occurrences defect similar to the classified defects .” Under the Step 1 of the eligibility analysis, we determine whether the claim is directed to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (apparatus for claim 1). Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the bold type portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes (concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions). In claim 1, the steps of “perform feature extraction to fuse the image data of the plurality of output signals to generate feature maps for respective portions of the battery electrode material” ; “generate binary information for each of the portions indicating whether the portions include one or more defects ”; “classify the defects based on the binary information and at least one of the feature maps and image data” ; “perform operations to discard the one or more portions of the battery electrode material which have one or more defects” , and “perform feedback process control operations to prevent future occurrences defect similar to the classified defects” are mathematical concepts, therefore, they are considered to be an abstract idea. The steps of “determine whether there are defects in the portions of the battery electrode material based on the feature maps” ; and “determine whether one or more of the portions of the battery electrode material which have one or more defects ought to be discarded” are a combination of a mathematical concept and a mental process, therefore, they are considered to be an abstract idea. Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. The claim comprises the following additional elements: a plurality of sensors of different types configured to scan battery electrode material and generate a plurality of output signals including respective image data; a feature extractor module configured to receive the plurality of output signals; and a control module configured to report the classified defects. The additional element “a feature extractor module configured to receive the plurality of output signals” represents necessary data gathering and does not integrate the limitation into a practical application. The additional elements “a plurality of sensors of different types configured to scan battery electrode material and generate a plurality of output signals including respective image data”; and “a control module configured to report the classified defects” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception. The additional elements “a feature extractor module”; “a coarse detector”; “a fine detector”, and “a control module” are not sufficient to integrate the abstract idea into a practical application because they are considered a generic computer element. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94. In conclusion, the above additional elements, considered individually and in combination with the other claims elements do not reflect an improvement to other technology or technical field, do not reflect improvements to the functioning of the computer itself, do not recite a particular machine, do not effect a transformation or reduction of a particular article to a different state or thing, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claim is directed to a judicial exception and require further analysis under the Step 2B. The above claim, does not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in a relevant art as evidenced by the prior art of record (Step 2B analysis). For example, a feature extractor module receives the plurality of output signals is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e., receiving signal data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). For example, a plurality of sensors of different types scan battery electrode material and generate a plurality of output signals including respective image data is disclosed by “Chandran US 20240377336”, Abstract, [0001], [0015], Claims 14 and 16; and “Yu CN 106706756A”, Abstract, [0009], [0015], [0018], [0035], Claim 2. The claim, therefore, is not patent eligible. Independent claim 11 recites subject matter that is similar or analogous to that of claim 1, and therefore, the claim is also patent ineligible. With regards to the dependent claims, claims 2-10 and 12-20 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application. The dependent claims are, therefore, also not patent eligible. 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 1, 6-7, 10-11, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chandran et al. (US 20240377336, hereinafter Chandran) in view of Ge et al. (US 20220189005, hereinafter Ge) . As to claims 1 and 11 , Chandran teaches a plurality of sensors of different types configured to scan battery electrode material and generate a plurality of output signals including respective image data ( [0001] discloses “a plurality of sensors to obtain defect measurements to monitor sheet materials, such as electrodes that are used in lithium-ion batteries” ); a feature extractor module configured to receive the plurality of output signals and perform feature extraction to fuse the image data of the plurality of output signals to generate feature maps for respective portions of the battery electrode material ( [0015] and [0016] disclose computer device receives sheet information signals from a plurality of sensors that measure one or more characteristics of the sheet, and a display device generates integrated images in a display screen which respond to sheet information from two or more of the plurality of sensors. The integrated quality measurement views include monitoring views, analysis views and reporting views, and the monitoring views can be used to see combined view of all defects aggregated from different systems; [0026] ); a coarse detector ( [0055] , i.e., a coarse detector can be implemented by a computer ) configured, based on the feature maps, to determine whether there are defects in the portions of the battery electrode material ( [0065] and [0066] disclose the defect maps are based on an electrode production process wherein the coated electrode has 250 points or bins across the width, and the monitoring 302 function of a monitor screen 502 can shows all defects, where the defect map view shows all defect data which are superimposed ), and a fine detector ( [0055] , i.e., a fine detector can be implemented by a computer ) configured, based on at least one of the feature maps and image data, to classify the defects ( [0076] discloses the surface defects are shown plotted on the defect map, and the top and bottom coats of the electrode the measurement and edge defects include: start point deviation, coating spots, edge voids and thin spots ( i.e., classify the defects - emphasis added by Examiner) for electrode production ); a control module configured to at least one of i) report the classified defects ( [0007] discloses the Quality Control System (QCS) scanning systems defects monitored for electrode production include deviations of the basis weight which are classified as: high-high, high, low-low and low. Icons for each of these defects for both the top coat and bottom coat as shown in FIG. 13 ), ii) determine whether one or more of the portions of the battery electrode material which have one or more defects ought to be discarded, iii) perform operations to discard the one or more portions of the battery electrode material which have one or more defects, and iv) perform feedback process control operations to prevent future occurrences defect similar to the classified defects. Chandran does not explicitly teach generate binary information for each of the portions indicating whether the portions include one or more defects; a detector configured, based on the binary information and at least one of the feature maps and image data, to classify the defects. Ge teaches generate binary information for each of the portions indicating whether the portions include one or more defects ( [0023] discloses a binary determination associated with a defect that determines whether the defect(s) of a certain type (or having certain characteristics) is present or absent in the target object or material in the battery, and the binary discriminator inference algorithm output a result indicative of presence of the defect(s) or an absence of the defect(s) ); a detector ( [0020] discloses AI based defect detector ) configured, based on the binary information and at least one of the feature maps and image data, to classify the defects ( [0023] discloses a binary determination associated with a defect indicates whether the defect(s) of a certain type or having certain characteristics is present or absent in the target object based on one or more characteristics of the plurality of training images received, and the battery defect characteristic can include one or more of electrode geometry overhang, delamination, foreign materials in the battery, welding defects, assembly defects ( i.e., classify the defects - emphasis added by Examiner)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ge into Chandran for the purpose of determining at least one defect is present or absent in the target object or battery electrode in order to efficiently detecting defects in the battery. This combination would improve accurately determining whether a battery electrode has indicative of presence of the defect or an absence of the defect by using the output of scanned images. As to claims 6 and 16 , the combination of Chandran and Ge teaches the claimed limitations as discussed in claims 1 and 11, respectively. Chandran teaches wherein the fine detector ( [0055] , i.e., a fine detector can be implemented by a computer ) analyzes portions of the at least one of the feature maps and image data associated with the portions of the battery electrode material that include defects ( [0065] and [0078] disclose the defect maps are based on an electrode production process. FIG. 14 shows the monitor screen 522 when the program is operating in the analysis function covering All Defects 400. The operator can view the historical data of the sheet, and there is a strip-wise filter which gives a high level view of the health of each strip. As shown in the region 400, the strip-wise filter shows each strip number 1-12 and the defect count in each strip ) and does not analyze portions of the at least one of the feature maps and image data associated with the portions of the battery electrode material that do not include defects ( [0078] FIG. 14 shows the monitor screen 522 when the program is operating in the analysis function covering All Defects 400. The operator can view the historical data of the sheet, and there is a strip-wise filter which gives a high level view of the health of each strip. As shown in the region 400, the strip-wise filter shows each strip number 1-12 and the defect count in each strip ( i.e., the monitor screen shows status all the battery strips, and thus, the computer could be programmed to analyze portions of the at least one of the feature maps and image data associated defects, and not to analyze portions of the feature maps and image data associated with the portions of the battery electrode material that do not include defects, since it is known for a computer to be capable of selectively analyzing data or map - emphasis added by Examiner)). As to claims 7 and 17 , the combination of Chandran and Ge teaches the claimed limitations as discussed in claims 1 and 11, respectively. Chandran teaches the control module ( [0055] ) is configured, prior to the coarse detector ( [0055] , i.e., a coarse detector can be implemented by a computer ) determining whether there are defects in portions of the battery electrode material, to perform a processing procedure on the image data received from the plurality of sensors ( [0015], [0059] and [0065] discloses the defect maps are based on an electrode production process; and the surface defect detection system analyzes the image data to detect surface defects ( i.e., the computer could be programmed to analyze the image data received from the plurality of sensors prior to the detector determining whether there are defects in portions of the battery electrode - emphasis added by Examiner)); the processing procedure comprises at least one of synchronizing the image data to generate processed data ( [0058] discloses all data generated from the various systems are synchronize in a data aggregator node which combines data from various sources for calculating product quality disposition at the unit level (cathode, anode etc.) ), aligning the image data, normalizing the image data, and cropping the image data; and the control module is configured to generate fused data of the feature maps based on the processed data ( [0061] discloses t he aggregator node (610) sequences the data based on time, encoder pulse and to transforms the data as required, and it combines data from surface defects detection system vision measurement system (600) and QCS server (614) ( i.e., generate combined data or fused data of the feature maps based on the processed data - emphasis added by Examiner)). As to claims 10 and 20 , the combination of Chandran and Ge teaches the claimed limitations as discussed in claims 1 and 11, respectively. Chandran teaches wherein the fine detector ( [0055] ; i.e., a fine detector can be implemented by a computer ) is configured to analyze portions of the at least one of the feature maps and image data containing at least one defect and to not analyze portions of the at least one of the feature maps and image data not containing a defect ( [0015], [0059] and [0065] discloses the defect maps are based on an electrode production process; and the surface defect detection system analyzes the image data to detect surface defects ( i.e., the detector can be implemented by a computer, and the computer would analyze portions of the feature maps and image data containing at least one defect, and the computer could be programmed not to analyze portions of the feature maps and image data not containing a defect - emphasis added by Examiner)) . 07-21-aia AIA Claim s 2-3 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Chandran and Ge, in view of Jarrahi et al. (US 20230016600, hereinafter Jarrahi) . As to claims 2 and 12 , the combination of Chandran and Ge teaches the claimed limitations as discussed in claims 1 and 11, respectively. Chandran teaches the plurality of sensors ( [0001] ). The combination of Chandran and Ge does not explicitly teach wherein the plurality of sensors comprise at least one surface scanning sensor, at least one interior scanning sensor, and at least one interface scanning sensor. Jarrahi teaches wherein the plurality of sensors comprise at least one surface scanning sensor, at least one interior scanning sensor, and at least one interface scanning sensor ( [0028], [0050], and [0052] disclose the sample comprises a battery electrode, and identification of chemical or structural variations which are material defects. FIG. 9 schematically illustrates a terahertz time-domain spectroscopy (THz-TDS) scanner ( i.e., interface scanning sensor - emphasis added by Examiner) setup in reflection mode modified with a confocal microscopy scheme and an integrated IR camera ( i.e., surface scanning sensor - emphasis added by Examiner) ; and FIGS. 11A through 11D provide X-ray CT images ( i.e., interior scanning sensor - emphasis added by Examiner)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jarrahi into Chandran in view of Ge for the purpose of determining material defects of a battery electrode in order to detect defects such as thickness variations, voids, cracks, non-uniformities, and any combination thereof. This combination would improve in accurately detecting irregularities in a battery electrode or a substrate. As to claims 3 and 13 , the combination of Chandran and Ge teaches the claimed limitations as discussed in claims 2 and 12, respectively. Chandran teaches the plurality of sensors ( [0001] ). The combination of Chandran and Ge does not explicitly teach the at least one surface scanning sensor comprises at least one of a camera, a laser 3D profiler, and a flash thermography scanner; the at least one interior scanning sensor comprises at least one of an X-ray scanner, a neutron imaging sensor, an eddy current sensor, and a beta gauging sensor; and the at least one interface scanning sensor comprises at least one of a terahertz scanner and an ultrasound sensor. Jarrahi teaches wherein: the at least one surface scanning sensor comprises at least one of a camera, a laser 3D profiler, and a flash thermography scanner ( [0028] and [0050] disclose the sample comprises a battery electrode, and identification of chemical or structural variations which are material defects; and FIG. 9 schematically illustrates a terahertz time-domain spectroscopy (THz-TDS) scanner setup in reflection mode modified with a confocal microscopy scheme and an integrated IR camera ( i.e., a camera can be used as a surface scanning sensor - emphasis added by Examiner)); the at least one interior scanning sensor comprises at least one of an X-ray scanner, a neutron imaging sensor, an eddy current sensor, and a beta gauging sensor ( ( [0028] and [0052] disclose the sample comprises a battery electrode, and identification of chemical or structural variations which are material defects; and FIGS. 11A through 11D provide X-ray CT images ( i.e., an X-ray scanner can be used as an interior scanning sensor - emphasis added by Examiner)); and the at least one interface scanning sensor comprises at least one of a terahertz scanner and an ultrasound sensor ( [0028], [0050], and [0124] disclose the sample comprises a battery electrode, and identification of chemical or structural variations which are material defects; and FIG. 9 schematically illustrates a terahertz time-domain spectroscopy (THz-TDS) scanner ( i.e., a terahertz scanner can be used as an interface scanning sensor - emphasis added by Examiner) setup in reflection mode modified with a confocal microscopy scheme and an integrated IR camera; and ultrasound time-of-flight imaging is a useful method for electrode defect detection ( i.e., interface scanning sensor comprises an ultrasound sensor - emphasis added by Examiner)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jarrahi into Chandran in view of Ge for the purpose of determining material defects of a battery electrode in order to detect defects such as thickness variations, voids, cracks, non-uniformities, and any combination thereof. This combination would improve in accurately detecting irregularities in a battery electrode or a substrate . 07-21-aia AIA Claim s 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Chandran and Ge, in view of Lan et al. (CN 117147574A, hereinafter Lan) . As to claims 4 and 14 , the combination of Chandran and Ge teaches the claimed limitations as discussed in claims 1 and 11, respectively. Chandran teaches the coarse detector ( [0055] , i.e., a coarse detector can be implemented by a computer ); and the fine detector ( [0055] , i.e., a fine detector can be implemented by a computer ). The combination of Chandran and Ge does not explicitly teach the detector is configured to implement a first convolutional neural network and perform deep learning to determine whether there are defects in the portions of the battery electrode material; and the detector is configured to implement a second convolutional neural network and perform deep learning to classify the defects. Lan teaches the detector is configured to implement a first convolutional neural network and perform deep learning to determine whether there are defects in the portions of the battery electrode material ( [0031] and [0064] disclose as shown in Figure 10, the first neural network model is a convolutional neural network ResNet, and the first stage scans the image and generates regions that may contain an object. The deep learning algorithm derives an optimal model, and the model locates and predicts defects in the captured images, calculates the defect positions ); and the detector is configured to implement a second convolutional neural network and perform deep learning to classify the defects ( [0031] and [0064] disclose as shown in Figure 10, the second neural network model is a convolutional neural network ResNet, and the second stage classifies the proposals and generates bounding boxes and masks. The deep learning algorithm derives an optimal model, and the model locates and predicts defects in the captured images, calculates the defect positions, and classifies the defects ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Lan into Chandran in view of Ge for the purpose of detecting a number of electrode layers defects of a battery in order to calculate a defect location, and classifies the defects. This combination would improve in accurately predicting defects of electrode layers of the battery by utilizing the deep learning algorithms of the optimal model so that the defect location can be calculated, and the defects can be classified . 07-21-aia AIA Claim s 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chandran and Ge, in view of Tran et al. (US 20220111960, hereinafter Tran) . As to claims 8 and 18 , the combination of Chandran and Ge teaches the claimed limitations as discussed in claims 7 and 17, respectively. Chandran teaches wherein the processing procedure includes synchronizing the image data ( [0058] discloses all data generated from the various systems are synchronize in a data aggregator node which combines data from various sources for calculating product quality disposition at the unit level (cathode, anode etc.) ). The combination of Chandran and Ge does not explicitly teach aligning the image data, normalizing the image data, and cropping the image data. Tran teaches aligning the image data, normalizing the image data, and cropping the image data ( [0130], [0210], and [0241] disclose with the multiple depth maps the system combines them to create a final mesh by calculating depth and projecting out of the camera registration ( i.e., aligning the image data - emphasis added by Examiner) ; the mobile irrigation system moves through a plot collecting and normalizing the color and NIR image data for the respective plot; and in FIG. 4A, the process trims the image ( i.e., cropping the image data - emphasis added by Examiner) to a relevant area, in this case the leaf, seed, or flower area ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Tran into Chandran in view of Ge for the purpose of analyzing image data for monitoring area, in order to less effect on the variation of image response from cloud cover induced variations in light intensity. This combination would improve consistency of the color and NIR image data resulting in more reliability of a defined response across a broader range of illuminance intensities. Examiner' s Note Regarding Claims 5, 9, 15, and 19 , the most pertinent prior arts are "Chandran US 20240377336", "Ueda US 20230065858", "Jarrahi US 20230016600", "Lan CN 117147574A", “Tran US 20220111960”, “Yu CN 106706756A”, “Alexandre US 6778694B1”, “Matsunami JP H01307876A”, “Oota US 20200265575”, “Tran US 20200364456”, “Cook US 20240272103”, and “Ge US 20220189005”. As to claims 5 and 15 , Chandran teaches defects includes edge defects such as start point deviation, coating spots, edge voids and thin spots, and coating void; and classifies defects in pre-defined categories and tags data with metadata for further processing (Chandran, [0059], [0065], [0076], [0077]). However, the prior arts of record, alone or in combination, do not fairly teach or suggest “wherein the fine detector is configured, when classifying the defects, to determine whether each of the defects is a dark band defect, a contamination defect, a crack, an embrittlement defect, a wrinkle, an edge imperfection, a polka dot, a delamination.” including all limitations as claimed. As to claims 9 and 19 , Chandran teaches wherein the fine detector ([0055]) is configured to perform multi-classification to identify defect types (Chandran, [0059] and [0077]). However, the prior arts of record, alone or in combination, do not fairly teach or suggest “localization to locate and scale bounding boxes to defects present in an image” including all limitations as claimed. Examiner notes, however, that claims 1-20 are rejected under 35 U.S.C. 101, and therefore, not patent eligible . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. “Ge US 20220189005” teaches “An inspection method includes receiving a plurality of training images and an image of a target object obtained from inspection of the target object. The method further includes generating, by one or more training codes, a plurality of inference codes. The one or more training codes are configured to receive the plurality of training images as input and output the plurality of inference codes. The one or more training codes and the plurality of inference codes includes computer executable instructions. The method further includes selecting one or more inference codes from the plurality inference codes based on a user input and/or one or more characteristics of at least a portion of the received plurality of training images. The method also includes inspecting the received image using the one or more inference codes of the plurality of inference codes.” Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAL CE MANG whose telephone number is (571)272- 0370. The examiner can normally be reached Monday to Friday- 8:30-12:00, 1:00-5:30 EST. 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, Catherine T Rastovski can be reached at (571) 270-0349. 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. /LAL CE MANG/Examiner, Art Unit 2857 Application/Control Number: 18/429,632 Page 2 Art Unit: 2857 Application/Control Number: 18/429,632 Page 3 Art Unit: 2857 Application/Control Number: 18/429,632 Page 4 Art Unit: 2857 Application/Control Number: 18/429,632 Page 5 Art Unit: 2857 Application/Control Number: 18/429,632 Page 6 Art Unit: 2857 Application/Control Number: 18/429,632 Page 7 Art Unit: 2857 Application/Control Number: 18/429,632 Page 8 Art Unit: 2857 Application/Control Number: 18/429,632 Page 9 Art Unit: 2857 Application/Control Number: 18/429,632 Page 10 Art Unit: 2857 Application/Control Number: 18/429,632 Page 11 Art Unit: 2857 Application/Control Number: 18/429,632 Page 12 Art Unit: 2857 Application/Control Number: 18/429,632 Page 13 Art Unit: 2857 Application/Control Number: 18/429,632 Page 14 Art Unit: 2857 Application/Control Number: 18/429,632 Page 15 Art Unit: 2857 Application/Control Number: 18/429,632 Page 16 Art Unit: 2857 Application/Control Number: 18/429,632 Page 17 Art Unit: 2857 Application/Control Number: 18/429,632 Page 18 Art Unit: 2857 Application/Control Number: 18/429,632 Page 19 Art Unit: 2857 Application/Control Number: 18/429,632 Page 20 Art Unit: 2857 Application/Control Number: 18/429,632 Page 21 Art Unit: 2857 Application/Control Number: 18/429,632 Page 22 Art Unit: 2857 Application/Control Number: 18/429,632 Page 23 Art Unit: 2857 Application/Control Number: 18/429,632 Page 24 Art Unit: 2857 Application/Control Number: 18/429,632 Page 25 Art Unit: 2857 Application/Control Number: 18/429,632 Page 26 Art Unit: 2857 Application/Control Number: 18/429,632 Page 27 Art Unit: 2857 Application/Control Number: 18/429,632 Page 28 Art Unit: 2857
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