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 .
Priority
Applicant claims the benefit of US Provisional Application No. 63616166, filed 12/29/2023. Claims 1-20 have been afforded the benefit of this filing date.
Information Disclosure Statement
The IDS dated 1/9/2025, 9/30/2025, and 6/18/2026 have been considered and placed in the application file.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 20 is/are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 20 recites “A special-purpose workstation computer configured to perform wallboard defect detection operations”. Due to the lack of recited structure in the limitations, the claim is indefinite, and will be interpreted as a claim with processes/steps. The examiner recommends amending this claim so that the computer contains structural elements to define the boundaries of the hardware.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites:
“accessing imaging data obtained from an imaging device configured to scan a wallboard” is a generically recited insignificant extra-solution activity of data outputting.
“processing the imaging data with a machine-learned defect detection model, the machine-learned defect detection model having been trained to generate board classification data indicative of one or more classifications associated with the wallboard” which can be reasonably interpreted as a human observer mentally classifying a wallboard in an image. A machine-learning model is simply adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
“generating one or more corrective action instructions relative to the wallboard based on the board classification data generated by the machine-learned defect detection model” which can be reasonably interpreted as a human observer with the aid of a pen and paper, creating instructions on how to fix a identified defect.
Claim 2 recites “wherein the imaging data obtained from the imaging device comprises thermal imaging data obtained from a linescanner”. This is a well-understood, routine, and conventional insignificant extra-solution activity of data gathering by a line-scanning camera.
Claim 3 recites “wherein the thermal imaging data comprises temperature values for respective pixels of a two-dimensional scan of the wallboard”. This is a well-understood, routine, and conventional insignificant extra-solution activity of thermal imaging.
Claim 4 recites “wherein the board classification data is indicative of one or more classifications associated with the wallboard comprises a binary classification indicative of the wallboard being either defect-free or having a defect”. A person can mentally determine if a wallboard is defect-free or contains a defect.
Claim 5 recites “wherein the board classification data is indicative of one or more classifications associated with the wallboard comprises a multi-class classification indicative of the wallboard having a particular type of defect from a predefined group of defect types”. A person can mentally observe a defect and determine how severe it is.
Claim 6 recites “wherein the predefined group of defect types comprises a first blister type or a second blister type”. A person can mentally determine how severe the blisters are in a defect.
Claim 7 recites “wherein the board classification data indicative of one or more classifications associated with the wallboard comprises a graphical representation associated with a detected defect”. This is a well-understood, routine, and conventional insignificant extra-solution activity of data outputting.
Claim 8 recites “wherein the graphical representation associated with the detected defect comprises a bounding shape encompassing the detected defect within the imaging data”. This is a well-understood, routine, and conventional insignificant extra-solution activity of image segmentation.
Claim 9 recites “wherein the machine-learned defect detection model comprises a convolutional neural network”. This is a well-understood, routine, and conventional insignificant extra-solution activity of a convolutional neural network.
Claim 10 recites “the machine-learned defect detection model having been trained using supervised learning based on a set of training data, wherein the training data comprises pairs of wallboard imaging data and corresponding labels identifying an associated classification for the wallboard imaging data”. This is a well-understood, routine, and conventional insignificant extra-solution activity of supervised learning.
Claim 11 recites “the machine-learned defect detection model having been trained using unsupervised learning based on a set of training data, wherein the training data comprises unlabeled wallboard imaging data, the machine-learned defect detection model having been trained to identify boards with defects as anomalies relative to non-defective boards”. This is a well-understood, routine, and conventional insignificant extra-solution activity of unsupervised learning.
Claim 12 recites “processing the imaging data with a board detection system configured to detect and localize individual board images within the imaging data; and wherein processing the imaging data with a machine-learned defect detection model comprises processing the individual board images with the machine-learned defect detection model”. A person can mentally detect and localize individual boards in an image. Inputting the individual board images into the model is a well-understood, routine, and conventional insignificant extra-solution activity of data inputting.
Claim 13 recites “wherein the one or more corrective action instructions relative to the wallboard comprises automatically diverting the wallboard into a waste area or an inspection area”. A person with the aid of pen and paper can write instructions to dispense a wallboard if it is defective.
Claim 14 recites “wherein the one or more corrective action instructions relative to the wallboard comprises automatically adjusting a step or parameter of a manufacturing process used to produce the wallboard”. A person with the aid of pen and paper can write instructions to adjust how to manufacture a wallboard.
Claim 15 corresponds to claims 1, additionally reciting a defect detection system. It is rejected under 35 U.S.C. 101 because the claim appears to be directed to a software embodiment, as it is simply a system with an ML model (i.e. software + an algorithm). Software and algorithms, alone, are not physical components and thus are not statutory since they do not define any structural and functional interrelationships between the computer programs and other claimed elements of a computer, which permit the computer's program functionality to be realized. Hence, the claim is entirely directed to software and is not directed to a hardware embodiment. If the claim were interpreted as having a hardware component, however, the claim would still be rejected under 35 U.S.C. 101 for the same reasons of being directed towards an abstract idea as claim 1.
Claims 16 and 18 corresponds to claim 1, additionally reciting a defect detection system similar to independent claim 15. The limitations are rejected for the same reasons of being directed towards an abstract idea as claim 1.
Claim 17 recites “a board detection system configured to process the imaging data and to detect and localize individual board images within the imaging data”. A person can mentally detect individual boards in an image.
Claim 19 recites “one or more mechanical or robotic components configured to execute the one or more corrective action instructions”. These parts are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Thus, they are rejected for the same reasons of being directed towards an abstract idea as claim 1.
Claim 20 corresponds to claim 1, additionally reciting a special-purpose workstation computer configured to perform wallboard defect detection operations. These parts are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Thus, they are rejected for the same reasons of being directed towards an abstract idea as claim 1.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 –
(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.
Claims 1, 4, 7-10, 12, 15-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bokshi-Drotar (US 10977490 B1).
Regarding claim 1, Bokshi-Drotar discloses a computer-implemented method for detecting wallboard defects (Bokshi-Drotar, Col. 9, Lines 30-34, "FIG. 9 depicts a block diagram of an exemplary computer-implemented method 900 in a processing server of analyzing image data to automatically assess hail damage to a property"), the computer-implemented method comprising:
accessing imaging data obtained from an imaging device configured to scan a wallboard (Bokshi-Drotar, Col. 4, Lines 15-18, "In operation, the image sensor(s) (or cameras) of the set of aerial vehicles 103, 104 may be configured to capture digital images that depict various portions of the properties 101, 102"),
processing the imaging data with a machine-learned defect detection model, the machine-learned defect detection model having been trained to generate board classification data indicative of one or more classifications associated with the wallboard (Bokshi-Drotar, Col. 3, Lines 6-12, "According to certain aspects, systems and methods may train a set of image models that may be used to classify property damage that may be caused by a hail event. Additionally, the systems and methods may capture and/or access digital image data that depicts a roof of the property, and analyze the digital image data using the trained image model"),
And generating one or more corrective action instructions relative to the wallboard based on the board classification data generated by the machine-learned defect detection model (Bokshi-Drotar, Col. 10, Lines 4-9, "The processing server may calculate (block 935), based on the set of outputs, an estimated damage amount to the roof of the property. Additionally, the processing server may facilitate any insurance processing, including a claim submission or policy modification, based on the estimated damage amount").
Regarding claim 4, Bokshi-Drotar discloses the computer-implemented method of claim 1, wherein the board classification data is indicative of one or more classifications associated with the wallboard comprises a binary classification indicative of the wallboard being either defect-free or having a defect (Boksshi-Drotar, Col. 7, Lines 53-58, "The binary classifier 406 may be configured to output a binary value (e.g., a “0” or “1”), where a positive binary value may be indicative of actual hail damage in the corresponding region (407) and a negative binary value may be indicative of an absence of hail damage in the corresponding region (408)").
Regarding claim 7, Bokshi-Drotar discloses the computer-implemented method of claim 1, wherein the board classification data indicative of one or more classifications associated with the wallboard comprises a graphical representation associated with a detected defect (Bokshi-Drotar, Col. 7, Lines 39-44, Fig. 4 below, "Accordingly, the representation 402 includes three (3) regions that correspond to the regions 403, 404, where two of the regions in the representation 402 correspond to the region 404. The regions of the representation 402 may represent a set of inputs for the classification model").
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Regarding claim 8, Bokshi-Drotar discloses the computer-implemented method of claim 7, wherein the graphical representation associated with the detected defect comprises a bounding shape encompassing the detected defect within the imaging data (Bokshi-Drotar, Col. 7, Lines 39-44, "Accordingly, the representation 402 includes three (3) regions that correspond to the regions 403, 404, where two of the regions in the representation 402 correspond to the region 404. The regions of the representation 402 may represent a set of inputs for the classification model", the outline in 402 covers the defect area).
Regarding claim 9, Bokshi-Drotar discloses the computer-implemented method of claim 1, wherein the machine-learned defect detection model comprises a convolutional neural network (Bokshi-Drotar, Col. 9, Lines 37-41, "The method 900 may begin when the processing server trains (block 905) a convolutional neural network (CNN) using a set of training data comprising a set of training images and a set of training labels").
Regarding claim 10, Bokshi-Drotar discloses the computer-implemented method of claim 1, the machine-learned defect detection model having been trained using supervised learning based on a set of training data, wherein the training data comprises pairs of wallboard imaging data and corresponding labels identifying an associated classification for the wallboard imaging data (Bokshi-Drotar, Col. 9, Lines 38-41, "The section 201 may include a set of training images 202, a set of training labels 203, and a hail damage detection model 204", supervised learning consists of labelled data).
Regarding claim 12, Bokshi-Drotar discloses the computer-implemented method of claim 1, further comprising: processing the imaging data with a board detection system configured to detect and localize individual board images within the imaging data; and wherein processing the imaging data with a machine-learned defect detection model comprises processing the individual board images with the machine-learned defect detection model (Bokshi-Drotar, Col. 9, Lines 50-53, " The processing server may analyze (block 920), using the CNN, the set of digital images to identify a set of regions of potential hail damage.").
Claims 15-16, 18 correspond to claim 1, additionally reciting a defect detection system (Bokshi-Drotar, Col. 1, Lines 58-60, “In another embodiment, a system for analyzing image data to automatically assess hail damage to a property is provided.”). The remaining limitations are rejected for the same reasons of anticipation as claim 1.
Regarding claim 17, Bokshi-Drotar discloses the defect detection system of claim 16, further comprising: a board detection system configured to process the imaging data and to detect and localize individual board images within the imaging data (Bokshi-Drotar, Col. 9, Lines 50-53, " The processing server may analyze (block 920), using the CNN, the set of digital images to identify a set of regions of potential hail damage.").
Regarding claim 19, Bokshi-Drotar discloses the defect detection system of claim 18, further comprising: one or more mechanical or robotic components configured to execute the one or more corrective action instructions (Bokshi-Drotar, Col. 10, Lines 54-59, "The server 1015 may further include a user interface 1081 configured to present information to a user and/or receive inputs from the user. As shown in FIG. 10, the user interface 1081 may include a display screen 1082 and I/O components 1083 (e.g., ports, capacitive or resistive touch sensitive input panels, keys, buttons, lights, LEDs, speakers, microphones)", display screens are mechanical components).
Claim 20 corresponds to claim 1, additionally reciting a special-purpose workstation computer (Bokshi-Drotar, Col. 4, Lines 33-36, “The system 100 may also include a server computer 115 that may communicate with the aerial vehicles 103, 104 and with the websites/internet services 106 via one or more networks 110”). The remaining limitations are rejected for the same reasons of anticipation as claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Bokshi-Drotar (US 10977490 B1) in view of Graff (US 20200280687 A1).
Regarding claim 2, Bokshi-Drotar discloses the computer-implemented method of claim 1.
Bokshi-Drotar does not teach “wherein the imaging data obtained from the imaging device comprises thermal imaging data obtained from a linescanner”.
However, Graff teaches wherein the imaging data obtained from the imaging device comprises thermal imaging data obtained from a linescanner (Graff, paragraph [0017], "The sensing device 28 may include, without limitation, any combination of thermometers, sensors, and/or charge-coupled devices (CCDs) capable of detecting, measuring, and/or evaluating on a pixel-by-pixel basis the intensity of the thermal radiation emitted from the glass gob 22. In one non-limiting example, the sensing device 28 may be a linescanning device having a one-dimensional sensing array, or a focal plane array sensor having a two-dimensional sensing array").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to capture Bokshi-Drotar’s images with thermal imaging using a linescanner, as taught by Graff.
The suggestion/motivation for doing so would have been to distinctly outline damages on properties and allow for easier identification in darker settings.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Bokshi-Drotar in view of Graf to obtain the invention as specified in claim 2.
Regarding claim 3, Bokshi-Drotar in view of Graf discloses the computer-implemented method of claim 2, wherein the thermal imaging data comprises temperature values for respective pixels of a two-dimensional scan of the wallboard (Graff, paragraph [0017], "The sensing device 28 may include, without limitation, any combination of thermometers, sensors, and/or charge-coupled devices (CCDs) capable of detecting, measuring, and/or evaluating on a pixel-by-pixel basis the intensity of the thermal radiation emitted from the glass gob 22. In one non-limiting example, the sensing device 28 may be a linescanning device having a one-dimensional sensing array, or a focal plane array sensor having a two-dimensional sensing array").
Claim(s) 5-6, 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Bokshi-Drotar (US 10977490 B1) in view of Toth (US 20200394784 A1).
Regarding claim 5, Bokshi-Drotar discloses the computer-implemented method of claim 1.
Bokshi-Drotar does not teach “wherein the board classification data is indicative of one or more classifications associated with the wallboard comprises a multi-class classification indicative of the wallboard having a particular type of defect from a predefined group of defect types”.
However, Toth teaches wherein the board classification data is indicative of one or more classifications associated with the wallboard comprises a multi-class classification indicative of the wallboard having a particular type of defect from a predefined group of defect types (Toth, paragraph [0012], "In some embodiments, the output report includes identification and classification of the defect and one or more of: a potential cause of the defect; severity of the defect; and a recommendation on how to address the defect. The classification data may include defect type (e.g. discoloration, fatigue, environmental) and/or severity (e.g. low risk, medium risk, and high risk)").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to assign a severity to Bokshi-Drotar’s hail damaged regions, as taught by Toth.
The suggestion/motivation for doing so would have been to provide further insight for claiming property damage insurance.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Bokshi-Drotar in view of Toth to obtain the invention as specified in claim 5.
Regarding claim 6, Bokshi-Drotar in view of Toth discloses the computer-implemented method of claim 5, wherein the predefined group of defect types comprises a first blister type or a second blister type (Toth, paragraph [0012], "In some embodiments, the output report includes identification and classification of the defect and one or more of: a potential cause of the defect; severity of the defect; and a recommendation on how to address the defect. The classification data may include defect type (e.g. discoloration, fatigue, environmental) and/or severity (e.g. low risk, medium risk, and high risk)", blister by definition means structures that bulge out. Hail damage causes dents or destruction in house properties and certain pieces bulge out as a result. By assigning a severity to these damages, a first and second blister type can be determined).
Regarding claim 13, Bokshi-Drotoar discloses the computer-implemented method of claim 1.
Bokshi-Drotar does not teach “wherein the one or more corrective action instructions relative to the wallboard comprises automatically diverting the wallboard into a waste area or an inspection area”.
However, Toth teaches wherein the one or more corrective action instructions relative to the wallboard comprises automatically diverting the wallboard into a waste area or an inspection area (Toth, paragraph [0045], "On the other hand, where the image analyzer 130 determines that the infrastructure feature is a broken cross-arm, the associated recommendation may be to take immediate remedial action (replacing the cross-arm) since the defect is considered high risk", using the concept of throwing away and replacing a damaged part, this can be considered as diverting the house property into a waste area).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to replace and fix Bokshi-Drotar’s hail damaged region, as taught by Toth.
The suggestion/motivation for doing so would have been to provide better safety and cover.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Bokshi-Drotar in view of Toth to obtain the invention as specified in claim 13.
Regarding claim 14, Bokshi-Drotoar discloses the computer-implemented method of claim 1.
Bokshi-Drotar does not teach “wherein the one or more corrective action instructions relative to the wallboard comprises automatically adjusting a step or parameter of a manufacturing process used to produce the wallboard”.
However, Toth teaches wherein the one or more corrective action instructions relative to the wallboard comprises automatically adjusting a step or parameter of a manufacturing process used to produce the wallboard (Toth, paragraph [0045], "On the other hand, where the image analyzer 130 determines that the infrastructure feature is a broken cross-arm, the associated recommendation may be to take immediate remedial action (replacing the cross-arm) since the defect is considered high risk", “manufacturing process” through broadest reasonable interpretation can include what to do when a part is broken).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to replace and fix Bokshi-Drotar’s hail damaged region, as taught by Toth.
The suggestion/motivation for doing so would have been to provide better safety and cover.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Bokshi-Drotar in view of Toth to obtain the invention as specified in claim 14.
Claim(s) 11 are rejected under 35 U.S.C. 103 as being unpatentable over Bokshi-Drotar (US 10977490 B1) in view of Lin (US 20220058787 A1).
Regarding claim 11, Bokshi-Drotar discloses the computer-implemented method of claim 1.
Bokshi-Drotar does not teach “the machine-learned defect detection model having been trained using unsupervised learning based on a set of training data, wherein the training data comprises unlabeled wallboard imaging data, the machine-learned defect detection model having been trained to identify boards with defects as anomalies relative to non-defective boards”.
However, Lin teaches the machine-learned defect detection model having been trained using unsupervised learning based on a set of training data, wherein the training data comprises unlabeled wallboard imaging data, the machine-learned defect detection model having been trained to identify boards with defects as anomalies relative to non-defective boards (Lin, paragraph [0029], "In some embodiments (e.g., in which the defect-attribute extraction 210 is not performed), target images from the optical inspection are provided (220) to an unsupervised machine-learning algorithm").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to perform unsupervised training on Bokshi-Drotar’s model, as taught by Lin.
The suggestion/motivation for doing so would have been to work with larger datasets as labeled data is not required.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Bokshi-Drotar in view of Lin to obtain the invention as specified in claim 11.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WAYNE ZHANG whose telephone number is (571) 272-0245. The examiner can normally be reached Monday-Friday 10:00-6:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ms. Sumati Lefkowitz can be reached on (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WAYNE ZHANG/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
