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 .
Information Disclosure Statement
The IDS(s) has/have been considered and placed in the application file.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Interpretation - 35 USC § 101
The claim could be viewed as reciting the abstract idea of "classifying data based on learned patterns" (ML classification). However, under Alice/Mayo and USPTO guidance (MPEP 2106.04(a)(2)), a claim is still eligible if the elements, considered as an ordered combination, integrate the exception into a practical application. Conclusion on Prong 1: The claim recites classification, a computational process judicial exception). However, the claim is not directed to the exception in the abstract; it is directed to a specific technological application: analyzing EL images of solar cells to detect defects. The claim emphasizes "performed by an electronic
apparatus" and "based on an electroluminescence (EL) image," establishing a technical
problem context.
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: “unit for” in claim(s) 6-8.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5, 6, 7, and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nisshinbo Holdings Inc., EP 2 141 505 A1 (hereinafter "Nisshinbo") in view of McScience Inc., KR 2020/0075141 A (hereinafter "McScience").
[Claim 1] (Original)
Nisshinbo discloses a method for determining whether a solar battery cell being inspected is defective, performed by an electronic apparatus and based on an electroluminescence (EL) image of the solar battery cell being inspected (Nisshinbo: "method of determining defects in photovoltaic devices” using “electroluminescence (EL) emission” caused by “applying a predetermined current to photovoltaic devices” and “the light emitted from each photovoltaic cell of the photovoltaic device is photographed cell by cell” (Abstract; ¶¶ 9, 56-58; Figs. 1, 3. This teaches an EL-based solar cell defect inspection method performed by an electronic apparatus.), comprising:
a step of classifying an EL image of a solar battery cell being inspected, according to whether a black spot occupies a certain percentage or more of the EL image of the solar battery cell being inspected, namely, primarily classifying the EL image of the solar battery cell being inspected into a first type in which a black spot occupies a certain percentage or more, or a second type in which a black spot occupies less than the certain percentage (Nisshinbo: "calculates a threshold value based on an average brightness of a region of the photographed image where
bright and dark parts are mixed " and "divides the photographed image into bright and dark regions based on the threshold value" and " determines existence of a defect for each photovoltaic cell " (Claim 1; ¶¶69-73, 78). This teaches primary classification of an EL image into a first type in which a dark region’s area exceeds threshold (i.e., defective) or a second type in which it does not (i.e., non-defective), based on the area of dark regions relative to a brightness threshold.); and
a step of secondarily classifying the type of defect of the solar battery cell being inspected, based on the EL image of the solar battery cell being inspected, by using a (Nisshinbo: " determines the existence of a defect and type of a defect according to defect types by previously classifying and registering the defect types" and classifying defects into “[finger break]," "[crack]," and "[fragment]" (Claim 2; ¶¶29, 37, 77). This teaches secondary classification of defect types for cells identified as defective in the primary classification step.),
Nisshinbo discloses all of the subject matter as described above except for specifically teaching “a pre-trained defect classification model … wherein the defect classification model is a model which is trained according to a machine learning technique by using learning data that respectively includes input data for learning EL images and result data for the type of defect of the solar battery cell of the learning EL images.” However, McScience in the same field of endeavor teaches a pre-trained defect classification model … wherein the defect classification model is a model which is trained according to a machine learning technique by using learning data that respectively includes input data for learning EL images and result data for the type of defect of the solar battery cell of the learning EL images (McScience: "an image classification model based on a pre-trained convolutional neural network may be prepared. The pre-trained convolutional neural network based image classification model may be learned based on images of a solar cell module in which a defect is detected" (¶¶ 27, 29, 52, Fig. 2). This teaches a defect classification model trained according to a machine learning technique using learning data including EL images as input data and defect type as result data.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the solar cell defect classification system of Nisshinbo to use a pre-trained convolutional neural network for defect type classification as taught by McScience. The motivation for this combination would have been to improve the accuracy and automation of defect classification by leveraging the pattern recognition capabilities of deep learning models trained on labeled EL image datasets, thereby reducing reliance on manually defined defect shape rules and enabling detection of complex or novel defect patterns (McScience ¶¶ 6, 27).
[Claim 2] (Original)
The method of claim 1, wherein the primarily classifying comprises a step of classifying the EL image of the solar battery cell being inspected by using a pre- trained image generation model according to a machine learning technique to generate an image (black spot image) for a black spot included in the EL image from the learning EL images.
[Claim 3] (Original)
The method of claim 2, wherein the image generation model is a model which is trained by using learning data that respectively includes input data for learning EL images and result data for the black spot image according to image processing of the learning EL images.
[Claim 4] (Original)
The combination of Nisshinbo and McScience discloses the method of claim 3, wherein the image processing includes histogram homogenization processing, bus-bar line removal processing, edge-based perspective transform processing and contour extraction processing for the learning EL images.
[Claim 5] (Original)
The method of claim 1, wherein the defect classification model classifies the types of defects generated in a plurality of different manufacturing processes by a plurality of causes (Nisshinbo teaches that defects are classified into "[finger break]," "[crack]," and "[fragment]," and specifically that cracks arise from "compression during a laminating process, or handling loads, or impacts during transportation, or during the module manufacturing process" and
separately from soldering when "lead wires are soldered onto the busbars" (¶¶ [0029][
0033], [0090]-[0091]; Figs. 9-11). This teaches classification of defect types arising from multiple causes across different manufacturing processes. McScience extends this by using a CNN-based image classification model that classifies defect types including "CL" type and "CX" type defects from EL images of solar cell modules (¶¶ [0027], [0029], [0039]-[0040]; Figs. 4A-4C). This teaches a defect classification model that classifies types of defects caused by different manufacturing processes.).
[Claim 6] (Original)
The combination of Nisshinbo and McScience discloses an apparatus, comprising: a memory for storing an EL image of a solar battery cell being inspected (Nisshinbo: "Programs or various types of data executed by the control unit 10 are stored in a memory 20" (11 [0035]; Fig. 1, element 20). This teaches a memory for storing EL images.); and a control unit for processing the stored EL image of the solar battery cell being inspected and controlling to analyze whether the solar battery cell being inspected is defective (Nisshinbo: "a control unit 10 is used to control the overall operations of the inspection apparatus and to determine whether a photovoltaic device passes inspection or not" (¶ [0035]; Fig. 1, element 10). This teaches a control unit for EL image processing and defect analysis.), wherein the control unit performs the primary classification and secondary classification steps as set forth in claim 1. The same reasoning and motivation as applied to claim 1 applies here.
[Claim 7] (Original)
The combination of Nisshinbo and McScience discloses an apparatus, comprising: a communication unit for receiving an EL image of a solar battery cell being inspected (Nisshinbo: "the control unit 10 controls the camera control unit 50 to photograph a photovoltaic cell 28 emitting EL light by using the camera 500; the photographed image is taken in the camera control unit 50 and is stored in ... the memory 20" (¶¶ [0038], [0058]; Fig. 1, element 50). This teaches a unit that receives EL image data from the imaging component for downstream processing); and a control unit for processing the received EL image of the solar battery cell being inspected and controlling to analyze whether the solar battery cell being inspected is defective, wherein the control unit performs the primary classification and secondary classification steps as set forth in claim 1. The same reasoning and motivation as applied to claim 1 applies here.
[Claim 8] (Previously Presented)
The combination of Nisshinbo and McScience discloses the apparatus of claim 6, wherein the control unit controls the execution of an operating program (Nisshinbo discloses a control unit 10 "configured by a personal computer" that executes programs and controls all operations of the inspection apparatus (¶ [0035]); McScience further explicitly teaches that the inspection embodiments "may be provided in the form of a computer program stored in a computer-readable storage medium to perform a method for inspecting a solar cell module" (McScience ,¶ [0056]). This teaches a control unit executing an operating program.), and wherein the operating program performs the analysis on the EL images of the solar battery cell being inspected according to time, date, cell ID or manufacturing line (Nisshinbo discloses sequential, cell-by-cell processing in which determination results are stored per cell in the external memory device 900 (¶¶ [0061]-[0063]). Organizing automated inspection analysis by metadata such as time, date, cell ID, or manufacturing line is a routine design choice in any automated inspection system of this type, well within the ordinary skill of the art. This teaches program-controlled analysis with per-cell result tracking that would ordinarily be organized by such identification metadata.).
Allowable Subject Matter
Claims 2, 3, and 4 are 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 prior art of record does not disclose or suggest, in combination with the other limitations of claim 2, "the primarily classifying comprises a step of classifying the EL image of the solar battery cell being inspected by using a pre-trained image generation model according to a machine learning technique to generate an image (black spot image) for a black spot included in the EL image from the learning EL images." While image generation models (e.g., GANs) are known in the art for data augmentation in solar cell inspection (see, e.g., Tang et al., "Deep learning based automatic defect identification of photovoltaic module using electroluminescence images," Solar Energy, 2020), the specific use of an image generation model to generate black spot images from EL images for the purpose of primary classification (i.e., determining black spot area occupancy) represents a non-obvious combination when linked to the training pipeline of the secondary defect classification model. Claims 3 and 4 depend from claim 2 and are allowable for the same reasons.
Conclusion
The prior art made of record but not relied, yet considered pertinent to the applicant’s disclosure, is listed on the PTO-892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ross Varndell whose telephone number is (571)270-1922. The examiner can normally be reached M-F, 9-5 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, O’Neal Mistry can be reached at (313)446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Ross Varndell/Primary Examiner, Art Unit 2674
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