DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
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.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because under the broadest reasonable interpretation (BRI) of the claim a “storage medium”, as claimed, covers both, statutory and non-statutory embodiments. A claim whose BRI covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. Examiner recommends amending the claim in order to recite a “non-transitory storage medium” to exclude non-statutory embodiments (See MPEP § 2106.03 II).
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “electrode plate wrinkling detection apparatus”, “processing module”, “training module”, “testing module”, “detection module”, “pre-processing module” in claims 7 and 8.
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 § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 7, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Peng et al. (“Automatic Internal Wrinkles Detection of Lithium-ion Batteries using Convolutional Neural Network”, 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), 23-27 August 2021, pp. 1422-1427) in view of Bufi et al. (U.S. Pub. No. 2022/0366558) and Chen et al. (U.S. Pub. No. 2023/0246249).
As to claims 1 and 7, Peng et al. teaches an electrode plate wrinkling detection method/system (i.e., “detection method based on X-ray technology and convolutional neural network(CNN) is proposed for internal wrinkles detection in LIBs”, Abstract, page 1422), comprising:
acquiring/electrode plate wrinkling detection apparatus configured to acquire (i.e., Fig. 2, “X-ray device”, page 1423) images of cells with wrinkled electrode plates and images of cells with non-wrinkled electrode plates (i.e., “X-ray device is employed to acquire LIBs four corners inner images with contrasts between light and dark for different thickness, which is the foundation of automatic internal wrinkles detection of LIBs using CNN”, A. Data Capture, page 1423;“The image dataset which are used in this study contains 2602 original X-ray images in total. All the images are obtained from X-ray device and grouped based on expert experience into two categories: unqualified-1098 images with serious internal wrinkles flaw, qualified-1504 images without any internal wrinkles flaw”, B. Classify images based on CNN, page 1423);
performing/processing module configured to: (i.e., “a computer with 80G RAM, Intel(R) Core(TM) i9-10980XE CPU, and NVIDIA GeForce RTX 2080Ti GPU”, C. Real-Time, page 1427) perform type labeling processing on the images of the cells with wrinkled electrode plates and the images of the cells with non-wrinkled electrode plates (i.e., “The image dataset which are used in this study contains 2602 original X-ray images in total. All the images are obtained from X-ray device and grouped based on expert experience into two categories: unqualified-1098 images with serious internal wrinkles flaw, qualified-1504 images without any internal wrinkles flaw”, B. Classify images based on CNN, page 1423), and constructing/construct a database by using the labeled images (i.e., “The image dataset which are used in this study contains 2602 original X-ray images in total”, B. Classify images based on CNN, page 1423; and “Shuffling samples sequence and randomly selecting 80 percent qualified images and 80 percent unqualified images to compose training set”, B. Classification performance comparison, page 1426);
a training module configured to: (i.e., “a computer with 80G RAM, Intel(R) Core(TM) i9-10980XE CPU, and NVIDIA GeForce RTX 2080Ti GPU”, C. Real-Time, page 1427) based on the database, training via a convolutional neural network to generate an electrode plate wrinkling detection model (i.e., 2. Training with three CNN architectures, page 1424);
testing and calibrating/testing module configured to (i.e., “a computer with 80G RAM, Intel(R) Core(TM) i9-10980XE CPU, and NVIDIA GeForce RTX 2080Ti GPU”, C. Real-Time, page 1427) test and calibrate the electrode plate wrinkling detection model (i.e., 3. Modify loss function to reduce false positive rate, pages 1424-1425); and
performing/detection module configured to perform (i.e., “a computer with 80G RAM, Intel(R) Core(TM) i9-10980XE CPU, and NVIDIA GeForce RTX 2080Ti GPU”, C. Real-Time, page 1427) electrode plate wrinkling detection on cell images obtained in a real-time manner by using the tested and calibrated electrode plate wrinkling detection model (See for example, C. Real-Time, page 1427).
However, Peng et al. does not explicitly disclose the testing and calibrating the model by using images outside the database, and the cell images obtained in a real-time manner are obtained during a battery winding process.
Bufi et al. teaches training and testing a model by using images outside a database (See for example, “the neural network 156 is generated using model training and conversion for inference which may include taking a pretrained model, training the pretrained model with new data”, Paragraph [0216]; and “Training of the defect detection model 154 may include image augmentation. Data augmentation may be performed to increase the variability of the input images, so that the designed object detection model has higher robustness to the images obtained from different environments”, Paragraph [0231]).
The combination of Peng et al. and Bufi et al. do not explicitly disclose the cell images obtained in a real-time manner are obtained during a battery winding process.
Chen et al. teaches cell images that are obtained in a during a battery winding process (See for example, “photographing module 50 and the winding shaft 40 are fixedly arranged at a certain distance in a third direction Z (as described hereinafter, the first direction X is perpendicular to the axial direction of the winding shaft 40, the second direction Y is the axial direction of the winding shaft 40, and the third direction Z is perpendicular to the first direction X and the second direction Y) to photograph the membrane layer composite 10 during winding so as to generate pictures”, Paragraph [0059]; and Paragraph [0060]).
Peng et al., Bufi et al. and Chen et al. are analogous art because they are from the field of digital image processing for image analysis.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Peng et al. by incorporating the testing and calibrating the model by using images outside the database, as taught by Bufi et al., and the cell images obtained in a real-time manner are obtained during a battery winding process, as taught by Chen et al.
The suggestion/motivation for doing so would have been to obtain a tested and calibrated model that has higher robustness to images obtained from different environments, and to avoid the risk of displacements between the electrode plates.
Therefore, it would have been obvious to combine Bufi et al. and Chen et al. with Peng et al. to obtain the invention as specified in claims 1 and 7.
As to claims 13 and 14, Peng et al. teaches a terminal (i.e., “a computer with 80G RAM, Intel(R) Core(TM) i9-10980XE CPU, and NVIDIA GeForce RTX 2080Ti GPU”, C. Real-Time, page 1427), comprising:
at least one processor (i.e., “a computer with 80G RAM, Intel(R) Core(TM) i9-10980XE CPU, and NVIDIA GeForce RTX 2080Ti GPU”, C. Real-Time, page 1427);
and the at least one processor to implement an electrode plate wrinkling detection method, comprising:
acquiring images of cells with wrinkled electrode plates and images of cells with non-wrinkled electrode plates (i.e., “X-ray device is employed to acquire LIBs four corners inner images with contrasts between light and dark for different thickness, which is the foundation of automatic internal wrinkles detection of LIBs using CNN”, A. Data Capture, page 1423;“The image dataset which are used in this study contains 2602 original X-ray images in total. All the images are obtained from X-ray device and grouped based on expert experience into two categories: unqualified-1098 images with serious internal wrinkles flaw, qualified-1504 images without any internal wrinkles flaw”, B. Classify images based on CNN, page 1423); performing type labeling processing on the images of the cells with wrinkled electrode plates and the images of the cells with non-wrinkled electrode plates (i.e., “The image dataset which are used in this study contains 2602 original X-ray images in total. All the images are obtained from X-ray device and grouped based on expert experience into two categories: unqualified-1098 images with serious internal wrinkles flaw, qualified-1504 images without any internal wrinkles flaw”, B. Classify images based on CNN, page 1423), and constructing a database by using the labeled images (i.e., “The image dataset which are used in this study contains 2602 original X-ray images in total”, B. Classify images based on CNN, page 1423; and “Shuffling samples sequence and randomly selecting 80 percent qualified images and 80 percent unqualified images to compose training set”, B. Classification performance comparison, page 1426); based on the database, training via a convolutional neural network to generate an electrode plate wrinkling detection model (i.e., 2. Training with three CNN architectures, page 1424); testing and calibrating the electrode plate wrinkling detection model (i.e., 3. Modify loss function to reduce false positive rate, pages 1424-1425); and performing electrode plate wrinkling detection on cell images obtained in a real-time manner by using the tested and calibrated electrode plate wrinkling detection model (See for example, C. Real-Time, page 1427).
However, Peng et al. does not explicitly disclose a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to implement the electrode plate wrinkling detection method/a storage medium storing a computer program, wherein the computer program is executed by a processor to implement the electrode plate wrinkling detection method, the testing and calibrating the model by using images outside the database, and the cell images obtained in a real-time manner are obtained during a battery winding process.
Bufi et al. teaches a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to implement a defect detection method/storage medium storing a computer program, wherein the computer program is executed by a processor to implement a defect detection method (See for example, Paragraphs [0071]-[0072]), training and testing a model by using images outside a database (See for example, “the neural network 156 is generated using model training and conversion for inference which may include taking a pretrained model, training the pretrained model with new data”, Paragraph [0216]; and “Training of the defect detection model 154 may include image augmentation. Data augmentation may be performed to increase the variability of the input images, so that the designed object detection model has higher robustness to the images obtained from different environments”, Paragraph [0231]).
The combination of Peng et al. and Bufi et al. do not explicitly disclose the cell images obtained in a real-time manner are obtained during a battery winding process.
Chen et al. teaches cell images that are obtained in a during a battery winding process (See for example, “photographing module 50 and the winding shaft 40 are fixedly arranged at a certain distance in a third direction Z (as described hereinafter, the first direction X is perpendicular to the axial direction of the winding shaft 40, the second direction Y is the axial direction of the winding shaft 40, and the third direction Z is perpendicular to the first direction X and the second direction Y) to photograph the membrane layer composite 10 during winding so as to generate pictures”, Paragraph [0059]; and Paragraph [0060]).
Therefore, in view of Bufi et al. and Chen et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Peng et al. by incorporating the memory communicatively connected to the at least one processor to store instructions executable by the at least one processor/storage medium storing a computer program, wherein the computer program is executed by a processor to implement the wrinkling detection model and the testing and calibrating the model by using images outside the database, as taught by Bufi et al., and the cell images obtained in a real-time manner are obtained during a battery winding process, as taught by Chen et al., in order to incorporate the method in a conventional manner to be executed in a computer, to obtain a tested and calibrated model that has higher robustness to images obtained from different environments, and to avoid the risk of displacements between the electrode plates.
Allowable Subject Matter
Claims 2-6 and 8-12 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the closest prior art made of record fails to disclose, teach, and/or suggest, inter alia, the electrode plate wrinkling detection of claims 1 and 7, and further comprising after the acquiring images of cells with wrinkled electrode plates and images of cells with non-wrinkled electrode plates, the electrode plate wrinkling detection method further comprises: pre-processing the acquired images of the cells with wrinkled electrode plates and images of the cells with non-wrinkled electrode plates, wherein the pre-processing comprises: performing cleansing on the images of the cells with wrinkled electrode plates and the images of the cells with non-wrinkled electrode plates, removing interference parts, and broadening, enhancing, and brightening the images of the cells with wrinkled electrode plates and the images of the cells with non-wrinkled electrode plates; or the performing type labeling processing on the images of the cells with wrinkled electrode plates and the images of the cells with non-wrinkled electrode plates, and constructing a database by using the labeled images comprises: labeling the images of the cells with wrinkled electrode plates and the images of the cells with non-wrinkled electrode plates with types comprising wrinkling of electrode plate, wrinkling of separator, stripe pattern on separator, stain on separator, and full qualification, and constructing a database by using the images labeled with the types comprising wrinkling of electrode plate, wrinkling of separator, stripe pattern on separator, stain on separator, and full qualification; or the testing and calibrating the electrode plate wrinkling detection model by using images outside the database comprises: testing the electrode plate wrinkling detection model by using the images outside the database, under a condition that a test result meets a target requirement, performing electrode plate wrinkling detection on cell images obtained in a real-time manner during a battery winding process by using the tested and calibrated electrode plate wrinkling detection model, or under a condition that the test result does not meet the target requirement, retraining the electrode plate wrinkling detection model by modifying parameters of the convolutional neural network until the test result meets the target requirement; or the performing electrode plate wrinkling detection on cell images obtained in a real-time manner during a battery winding process by using the tested and calibrated electrode plate wrinkling detection model comprises: shooting to-be-inspected cells during a winding process to obtain images of the cells, inputting the images of the cells into the electrode plate wrinkling detection model for detection, when determining that defective cells with wrinkled electrode plates exist, discharging the defective cells into a defective product tank, or when determining that no defective cell with wrinkled electrode plates exists, discharging normal cells into a next production procedure, as claimed.
Conclusion
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/JOSE M TORRES/Examiner, Art Unit 2664 04/16/2026
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