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 . 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.
DETAILED ACTION
The following Non-Final Office Action is in response to Request for Continued Examination filed on 2/4/2026.
Priority
The Examiner has noted the Applicants claiming Priority from Provisional Application 63/507,842 filed 06/13/2023.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on MM/DD/YYYY has been entered.
Status of Claims
Claims 1, 3-14, 17-20, 22-23 are currently pending.
Claims 1, 14, 18 are currently amended.
Claims 2, 15, 16 are previously cancelled.
Claim 21 is currently cancelled.
Claims 1, 3-14, 17-20, 22-23 are currently under examination and have been rejected as follows.
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Response to Amendment
The previously pending rejections under 35 USC 101 will be maintained. The 101 rejection is updated in view of the amendments.
The previously pending rejections under 35 USC 103 will be maintained. The 101 rejection is updated in view of the amendments.
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Response to Arguments
Regarding Applicant’s remarks pertaining to 35 USC 101:
Step 2A Prong 1:
Applicant argues on page 10 of remarks 2/4/2026:
“Applicant respectfully submits that, even if the current claim limitations were considered to involve an exception, these claim limitations do not recite any fundamental economic practices. Rather, the claims recite training and executing a computer vision model using machine learning, which is not a fundamental economic practice.”
Examiner respectfully disagrees. Examiner submits that the claims as amended as a whole still recite, describe or set forth assessing user vehicle damage, prescribing remedies, and scheduling appointments with users, which can be seen an insurance practices under the fundamental economic principles abstract subgrouping. However, arguendo, the claims were to be interpreted only to involve this abstract subgrouping rather than recite, describe, or set it forth, the abstract subgrouping of commercial or legal interactions, including agreements in the form of contracts, sales activities, and business relations still applies. Furthermore, assessing user vehicle damage, prescribing remedies, and scheduling appointments with users are also concepts which can be performed in the human mind, whether or not computers are used as an aide. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2:
Applicant argues beginning on page 10 of remarks 2/4/2026:
“Thus, Claim 1 represents improvements in capabilities of computer vision models to identify damage characteristics from an image and for a computer device to generate a recommendation for repairing glass based upon these identified damage characteristics.”
Continued on page 11: “Accordingly, in this case, ‘the specification sets forth an improvement in technology ... [and] the claim includes the components or steps of the invention that provide the improvement described in the specification,’ which is sufficient to establish a practical application.”
Examiner respectfully disagrees. Applicant specification does not appear to significantly elaborate on algorithmic or technological functionality regarding image analysis. To the extent technological details are provided, Examiner notes Applicant specification ¶ [0064]: “The damage severity engine may be based upon an algorithm and/or a machine-learning model, which may be trained using a machine-learning training/validation engine. The machine-learning training/validation engine may generate, train, and/or deploy a machine-learning model using historical claims data including, but not limited to, images of different types of damage, previous claim processing and/or audit history, and so on.… The machine-learning model may be a computer vision model trained to detect a damage on a windshield, a size/dimension of the damage, a type of damage, and/or a location of damage on the windshield, and so on”; and ¶ [0076]: “…a processing element may be trained by providing it with a large sample of images with known characteristics or features. Such information may include, for example, information associated with a plurality of images of a plurality of different objects, items, and/or property”. Examiner finds some evidence here of algorithmic evaluation of image data, but specific technological details are lacking to support the technological improvement to computer vision models asserted. These functions can be viewed as not meaningfully different than a business method or mathematical algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i).
Step 2B:
Applicant argues beginning on page 11 of remarks 2/4/2026:
“In the instant Application, the pending claims clearly recite more than well-understood, routine, or conventional functionality systems for user mobility analytics, at least with respect to at least one processor programmed to ‘train, via machine learning, a computer vision model based upon historical data, the historical data including (a) a plurality of historical images of window glass damage, and (b) historical characteristics associated with each of the plurality of historical images, the historical characteristics including at least (i) a historically determined type of damage associated with the corresponding historical image, and (ii) a historically determined size of damage associated with the corresponding historical image, the computer vision model configured to output characteristics of damage based upon an input of one or more images based upon one or more identified correlations between the plurality of historical images and the historical characteristics,’ as recited in amended Claim 1.”
Examiner respectfully disagrees. Examiner is unclear of the meaning of “user mobility analytics” in the context of the claims or specification. Applicant specification enumerates a plurality of conventional machine learning methods at ¶ [0075] including “linear or logistic regression, instance-based algorithms, regularization algorithms, decision trees, Bayesian networks, cluster analysis, association rule learning, artificial neural networks, deep learning, combined learning, reinforced learning, dimensionality reduction, and support vector machines”, examples of which are applied to image analysis at ¶ [0038] including: “By way of a non-limiting example, the machine-learning model may be a computer vision model, which may have been trained to identify one or more objects and/or their dimensions by analysis of an image.” Conventional analysis methods for windshield damage and recommended actions are documented in Examiner cited reference Emeka within the peer-reviewed International Journal of Trend in Scientific Research and Development (see 103 rejection section below for more details).
Accordingly, the previously pending rejections under 35 USC 101 will be maintained. The 101 rejection is updated in view of the amendments.
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Regarding Applicant’s remarks pertaining to 35 USC 103:
Applicant argues on page 13 of remarks 2/4/2026:
“No combination of Larson and Laskowski describes or suggests a computing device as
recited in amended Claim 1. More specifically, no combination of Larson and Laskowski describes or suggests at least one processor programmed to ‘train, via machine learning, a computer vision model based upon historical data, the historical data including (a) a plurality of historical images of window glass damage, and (b) historical characteristics associated with each of the plurality of historical images, the historical characteristics including at least (i) a historically determined type of damage associated with the corresponding historical image, and (ii) a historically determined size of damage associated with the corresponding historical image, the computer vision model configured to output characteristics of damage based upon an input of one or more images based upon one or more identified correlations between the plurality of historical images and the historical characteristics.’ Larson and Laskowski are silent with respect to training a computer vision model using historical data, and specifically, training a machine learning model using correlations between historical images and the specific types of historically determined characteristics (‘(i) a historically determined type of damage,’ and ‘(ii) a historically determined size of damage’) recited in Claim 1.”
Examiner respectfully disagrees. Additional support for the claim limitations as amended above can be found in primary reference Larson. In addition to previous citations teaching the independent claim in ¶s [0048], [0074], [0075], [0094], see Larson mid-¶ [0056]: “The server can process the images to assess damage, obtain information to assist with determination and/or condition of repair costs, process a claim, detect fraud, and/or train the system to better review future images”; end-¶ [0074]: “Also, the module can be configured to apply these same learning techniques to image capture and/or augmentation that is used to initiate vehicle glass damage identification and/or repair determination. For example, the preparation of additional images from single images of damage and the comparison of those images to image rules. The image rules including but not limited to previously categorized images of window damage”; Fig. 4D elements 72, 74: A.I. photo capture, classification and size measurement of damage; and Fig. 19 elements 509-510: photos captured and uploaded to A.I.
Additional amendments to claim limitations not specified in Applicant remarks are taught by the presented references and citations are included in the 103 rejection section below.
Accordingly, the rejection under 35 USC 103 is maintained.
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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, 3-14, 17-20, 22-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 3-13, 22-23 are directed to a device or machine which is a statutory category.
Claims 14, 17 are directed to a method or process which is a statutory category.
Claims 18-20 are directed to a non-transitory computer-readable storage media or article of manufacture which is a statutory category.
Step 2A Prong One: The claims recite, describe, or set forth a judicial exception of an abstract idea (see MPEP 2106.04(a)). Specifically, the claims recite, describe or set forth insurance, agreements in the form of contracts, sales activities, business relations, observation, evaluation, and judgement including: “receive… data representing a glass related damage claim”, “determine characteristics of the damage to the glass”, “determine a type of repair to fix the damage”, “communicate… the determined type of repair to fix the damage”, “prompt a user… to accept the type of repair including facilitating scheduling of an appointment to fix the damage”. Assessing user vehicle damage, prescribing remedies, and scheduling appointments with users fall within fundamental economic principles or practices, specifically insurance; as well as commercial or legal interactions, specifically agreements in the form of contracts and sales activities, each under the larger abstract grouping of Certain Methods of Organizing Human Activity (MPEP 2106.04(a)(2) II); as well as observation, evaluation, and judgement as they pertain to Mental Processes1 (MPEP 2106.04(a)(2) III). Examiner also points to MPEP2106.04(a)(2) III C finding that computer aided processes such as: 1. Performing a mental process on a generic computer, 2. Performing a mental process in a computer environment, and 3. Using a computer as a tool to perform a mental process can still be considered to recite a mental process.
Accordingly, the claims recite an abstract ideal.
Step 2A Prong Two: Independent claims 1, 14, 17 recite the following additional elements: “damage assessment computing device”, “memory”, “processor”, “computer vision model”, “machine learning model”, “client device”, and “non-transitory computer-readable storage media”. The functions of these additional elements include examples such as “receive… data representing a glass related damage claim”, “output characteristics of damage to the glass”, “analyzing the received data”, “output a type of repair”. The additional elements are recited at a high level of generality (i.e. as a generic computer performing functions of receiving and communicating data, analyzing, classifying, and correlating data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Therefore, these functions can be viewed as not meaningfully different than a business method or mathematical algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i). The claims are directed to an abstract idea and the judicial exception does not integrate the abstract idea into a practical application.
Step 2B: According to MPEP 2106.05(f)(1), considering whether the claim recites only the idea of a solution or outcome i.e., the claims fail to recite the technological details of how the actual technological solution to the actual technological problem is accomplished. The recitation of claim limitations that attempt to cover an entrepreneurial and thus abstract solution to an entrepreneurial problem with no technological details on how the technological result is accomplished and no description of the mechanism for accomplishing the result do not provide significantly more than the judicial exception.
The language “machine-learning model” merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea. The additional element is also recited at a high level of generality (i.e. as a generic computer performing functions of analyzing, classifying, and correlating data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components.
Dependent claims 3-13, 17, 20, 22-23 do not appear to provide any further additional computer-based elements, let alone for such additional computer-based elements to integrate the abstract idea into practical application (Step 2A Prong Two) or providing significantly more (Step 2B).
Further, dependent claims 3-13, 17, 20, 22-23 merely incorporate the additional elements recited in claims 1, 10 along with further narrowing of the abstract idea of claims 1, 10 along with their execution of the abstract idea. Specifically, the dependent claims narrow the “damage assessment computing device”, “memory”, “processor”, “client device”, and “non-transitory computer-readable storage media” to capabilities such as identifying, determining, comparing, prompting, and scheduling various forms of data such as damage type, repair type, crack length, threshold length, damage characteristics, location, repair types, appointments, etc. which, when evaluated per MPEP 2106.05(f)(2) represent mere invocation of computers to perform existing processes. Therefore, the additional elements recited in the claimed invention individually and in combination fail to integrate a judicial exception into a practical application (Step 2A prong two) and for the same reasons they also fail to provide significantly more (Step 2B). Thus, claims 1, 3-14, 17-20, 22-23 are reasoned to be patent ineligible.
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REJECTIONS BASED ON PRIOR ART
Examiner Note: Some rejections will contain bracketed comments preceded by an “EN” that will denote an examiner note. This will be placed to further explain a rejection.
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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 of this title, 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.
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.
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.
Claims 1, 3-4, 7-14, 17-20, 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over:
Larson US 20230214936 A1, hereinafter Larson, in view of
Laskowski US 20230019606 A1, hereinafter Laskowski. As per,
Regarding claim 1: Larson teaches:
(claim 1) A damage assessment (DA) computing device comprising at least one memory and at least one processor in communication with the at least one memory (Larson ¶ [0048]), wherein the at least one processor is programmed to:
train, via machine learning, a computer vision model based upon historical data, the historical data including (a) a plurality of historical images of window glass damage, and (b) historical characteristics associated with each of the plurality of historical images, the historical characteristics including at least (i) a historically determined type of damage associated with the corresponding historical image, and (ii) a historically determined size of damage associated with the corresponding historical image, the computer vision model configured to output characteristics of damage based upon an input of one or more images based upon one or more identified correlations between the plurality of historical images and the historical characteristics (Larson ¶ [0056]: Larson mid-¶ [0056]: The server can process the images to assess damage, obtain information to assist with determination and/or condition of repair costs, process a claim, detect fraud, and/or train the system to better review future images. End-¶ [0074]: “Also, the module can be configured to apply these same learning techniques to image capture and/or augmentation that is used to initiate vehicle glass damage identification and/or repair determination. For example, the preparation of additional images from single images of damage and the comparison of those images to image rules. The image rules including but not limited to previously categorized images of window damage. [Also see Fig. 4D elements 72, 74: A.I. photo capture, classification and size measurement of damage; and Fig. 19 elements 509-510: photos captured and uploaded to A.I.; and ¶s [0048], [0074], [0075], [0094]]);
receive, from a client device communicatively coupled with the DA computing device, data including one or more images of a glass of a window (Larson mid-¶ [0048]: The image(s) captured utilizing camera 18 can be then transferred or uploaded to processing circuitry 20, which includes a database 22 operably coupled to software 24 and hardware 26. ¶ [0074]: Module 34 is entitled "FNOL" (First Notice of Loss) can be filed using but not limited to a carrier web site, carrier app, or NCS interactive voice response system, for example. …a description of the damages is requested; appearance, size, and quantity…. Once the claim has been verified, the methods of the present disclosure can prompt the insured for required photos…. also the Machine Learning and Training module 36 can utilize images acquired to determine the presence of a repairable chip; the presence of a repairable crack; or the presence of a chip and/or crack that cannot be repaired and requires glass replacement);
input the received data including the one or more images of the glass of the window into the trained computer vision model, wherein the computer vision model outputs characteristics of
damage of the glass of the window based upon the input, the characteristics of the damage including (i) a type of the damage, and (ii) a size of the damage (Larson mid-¶ [0074]: … also the Machine Learning and Training module 36 [EN: computer vision model] can utilize images acquired to determine the presence of a repairable chip; the presence of a repairable crack; or the presence of a chip and/or crack that cannot be repaired and requires glass replacement…. Also, the module can be configured to apply these same learning techniques to image capture and/or augmentation that is used to initiate vehicle glass damage identification and/or repair determination. For example, the preparation of additional images from single images of damage and the comparison of those images to image rules. The image rules including but not limited to previously categorized images of window damage [EN: historical images and characteristics]. End-¶ [0075]: … computer vision can be used to prepare extracts of glass damage information that describes the morphology and structure of glass damage via tagging tasks that provide for labeling of specific features (size and shape) of a given image. Classification of a given image can be based on specific detection tasks that are tied to pixel-level precision for image detection. Detection is followed with segmentation into pre-defined glass damage buckets based on ROLAGS standards. ¶ [0094]: Referring next to FIG. 13, an example implementation of a machine learning relating to the images is shown, wherein images are received in step 300, and the damage classified in step 302 by the type of break at 304, the size of the break in 306, the number of breaks per image in 308, and the location of the breaks on the windshield itself in 310);
input the characteristics of the damage output by the computer vision model into a machine learning model that outputs a type of repair to fix the damage, the machine learning model trained based upon the historical characteristics and historical types of repair performed in response to the historical characteristics (Examiner interprets type of repair to include repairing or replacing the damaged windshield, consistent with Applicant specification ¶ [0032, 0071]. See Larson Table 1, referred to as a truth table, indicating type of repair determined, repair or replace, based on the size and type of the windshield damage. Larson mid-¶ [0094]: This machine learning is done first with data augmentation at 312 (example implementations are described with reference to FIGS. SE and 7B), and then the system fits received data to model in 314 and compared to the truth table as described, for example, in FIGS. 12A and 12B, at step 316, for a final disposition regarding fraud and/or replacement or repair of the windshield at 318. End-¶ [0074]: Also, the module can be configured to apply these same learning techniques to image capture and/or augmentation that is used to initiate vehicle glass damage identification and/or repair determination. For example, the preparation of additional images from single images of damage and the comparison of those images to image rules. The image rules including but not limited to previously categorized [EN: historical] images of window damage. End-¶ [0075]: Classification of a given image can be based on specific detection tasks that are tied to pixel-level
precision for image detection. Detection is followed with segmentation into pre-defined glass damage buckets [EN: historical characteristics / repair types] based on ROLAGS standards);
[..]
Although Larson teaches receiving data for a glass damage claim, determining the type and size of the damage, and determining how to address the damage, Larson does not specifically teach communicating the damage and repair assessment with a user.
However, Laskowski in analogous art of windshield damage repair teaches or suggests:
cause the client device to present the output type of repair to a user of the client device (Laskowski ¶ [0018]: Detection of cracks or chips in the windshield can be used to increase the safety in the vehicle and increase convenience for the user, driver, or owner of a vehicle. The present disclosure includes techniques for automatically scheduling a windshield repair or replacement [EN: see Applicant specification ¶ [0032, 0071] for types of repair including repair or replacement] based on detected damage. In an example, the detection of damage to the windshield could notify the driver either through a Heads-Up Display (HUD), alternative vehicle display, or a separate device personally accessible to the user or owner of the vehicle).
Laskowski and Larson are found as analogous art of windshield damage repair. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Larson’s motor vehicle glass insurance claim system and method to have included Laskowski’s teachings around communicating the damage and repair assessment with a user. The benefit of these additional features would have provided additional convenience for the user/insured (Laskowski ¶ [0018]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Larson in view of Laskowski (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of windshield damage repair. In such combination each element would have merely performed same organizational and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Larson in view of Laskowski above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claim 14: Larson teaches:
A computer-implemented (Larson ¶ [0048]) method, comprising:
training, by a damage assessment (DA) computing device, via machine learning, a computer vision model based upon historical data, the historical data including (a) a plurality of historical images of window glass damage, and (b) historical characteristics associated with each of the plurality of historical images, the historical characteristics including at least (i) a historically determined type of damage associated with the corresponding historical image, and (ii) a historically determined size of damage associated with the corresponding historical image, the computer vision model configured to output characteristics of damage based upon an input of one
or more images based upon one or more identified correlations between the plurality of historical
images and the historical characteristics (Larson ¶ [0056]: Larson mid-¶ [0056]: The server can process the images to assess damage, obtain information to assist with determination and/or condition of repair costs, process a claim, detect fraud, and/or train the system to better review future images. End-¶ [0074]: “Also, the module can be configured to apply these same learning techniques to image capture and/or augmentation that is used to initiate vehicle glass damage identification and/or repair determination. For example, the preparation of additional images from single images of damage and the comparison of those images to image rules. The image rules including but not limited to previously categorized images of window damage. [Also see Fig. 4D elements 72, 74: A.I. photo capture, classification and size measurement of damage; and Fig. 19 elements 509-510: photos captured and uploaded to A.I.; and ¶s [0048], [0074], [0075], [0094]]);
receiving, at the DA computing device from a client device communicatively coupled with the DA computing device, data including one or more images of a
glass of a window (Larson mid-¶ [0048]: The image(s) captured utilizing camera 18 can be then transferred or uploaded to processing circuitry 20, which includes a database 22 operably coupled to software 24 and hardware 26. ¶ [0074]: Module 34 is entitled "FNOL" (First Notice of Loss) can be filed using but not limited to a carrier web site, carrier app, or NCS interactive voice response system, for example. …a description of the damages is requested; appearance, size, and quantity…. Once the claim has been verified, the methods of the present disclosure can prompt the insured for required photos…. also the Machine Learning and Training module 36 can utilize images acquired to determine the presence of a repairable chip; the presence of a repairable crack; or the presence of a chip and/or crack that cannot be repaired and requires glass replacement);
inputting, by the DA computing device, the received data including the one or more images
of the glass of the window into the trained computer vision model, wherein the computer vision model outputs characteristics of damage of the glass of the window based upon the input, the characteristics of the damage including (i) a type of the damage, and (ii) a size of the damage (Larson mid-¶ [0074]: … also the Machine Learning and Training module 36 [EN: computer vision model] can utilize images acquired to determine the presence of a repairable chip; the presence of a repairable crack; or the presence of a chip and/or crack that cannot be repaired and requires glass replacement…. Also, the module can be configured to apply these same learning techniques to image capture and/or augmentation that is used to initiate vehicle glass damage identification and/or repair determination. For example, the preparation of additional images from single images of damage and the comparison of those images to image rules. The image rules including but not limited to previously categorized images of window damage [EN: historical images and characteristics]. End-¶ [0075]: … computer vision can be used to prepare extracts of glass damage information that describes the morphology and structure of glass damage via tagging tasks that provide for labeling of specific features (size and shape) of a given image. Classification of a given image can be based on specific detection tasks that are tied to pixel-level precision for image detection. Detection is followed with segmentation into pre-defined glass damage buckets based on ROLAGS standards. ¶ [0094]: Referring next to FIG. 13, an example implementation of a machine learning relating to the images is shown, wherein images are received in step 300, and the damage classified in step 302 by the type of break at 304, the size of the break in 306, the number of breaks per image in 308, and the location of the breaks on the windshield itself in 310);
inputting, by the DA computing device, the characteristics of the damage output by the computer vision model into a machine learning model that outputs a type of repair to fix the damage, the machine learning model trained based upon the historical characteristics and historical types of repair performed in response to the historical characteristics (Examiner interprets type of repair to include repairing or replacing the damaged windshield, consistent with Applicant specification ¶ [0032, 0071]. See Larson Table 1, referred to as a truth table, indicating type of repair determined, repair or replace, based on the size and type of the windshield damage. Larson mid-¶ [0094]: This machine learning is done first with data augmentation at 312 (example implementations are described with reference to FIGS. SE and 7B), and then the system fits received data to model in 314 and compared to the truth table as described, for example, in FIGS. 12A and 12B, at step 316, for a final disposition regarding fraud and/or replacement or repair of the windshield at 318. End-¶ [0074]: Also, the module can be configured to apply these same learning techniques to image capture and/or augmentation that is used to initiate vehicle glass damage identification and/or repair determination. For example, the preparation of additional images from single images of damage and the comparison of those images to image rules. The image rules including but not limited to previously categorized [EN: historical] images of window damage. End-¶ [0075]: Classification of a given image can be based on specific detection tasks that are tied to pixel-level precision for image detection. Detection is followed with segmentation into pre-defined glass damage buckets [EN: historical characteristics / repair types] based on ROLAGS standards);
[..]
Although Larson teaches receiving data for a glass damage claim, determining the type and size of the damage, and determining how to address the damage, Larson does not specifically teach communicating the damage and repair assessment with a user.
However, Laskowski in analogous art of windshield damage repair teaches or suggests:
causing, by the DA computing device, the client device to present the output type of repair to a user of the client device (Laskowski ¶ [0018]: Detection of cracks or chips in the windshield can be used to increase the safety in the vehicle and increase convenience for the user, driver, or owner of a vehicle. The present disclosure includes techniques for automatically scheduling a windshield repair or replacement [EN: see Applicant specification ¶ [0032, 0071] for types of repair including repair or replacement] based on detected damage. In an example, the detection of damage to the windshield could notify the driver either through a Heads-Up Display (HUD), alternative vehicle display, or a separate device personally accessible to the user or owner of the vehicle).
Laskowski and Larson are found as analogous art of windshield damage repair. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Larson’s motor vehicle glass insurance claim system and method to have included Laskowski’s teachings around communicating the damage and repair assessment with a user and prompting the user for acceptance and facilitating scheduling the service appointment. The benefit of these additional features would have provided additional convenience for the user/insured (Laskowski ¶ [0018]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Larson in view of Laskowski (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of windshield damage repair. In such combination each element would have merely performed same organizational and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Larson in view of Laskowski above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claim 18: Larson teaches:
A non-transitory computer-readable storage media having computer- executable instructions embodied thereon (Larson ¶ [0048]), wherein when executed by at least one processor, the computer-executable instructions cause the at least one processor to:
train, via machine learning, a computer vision model based upon historical data, the historical data including (a) a plurality of historical images of window glass damage, and (b) historical characteristics associated with each of the plurality of historical images, the historical characteristics including at least (i) a historically determined type of damage associated with the
corresponding historical image, and (ii) a historically determined size of damage associated with
the corresponding historical image, the computer vision model configured to output characteristics of damage based upon an input of one or more images based upon one or more identified correlations between the plurality of historical images and the historical characteristics (Larson ¶ [0056]: Larson mid-¶ [0056]: The server can process the images to assess damage, obtain information to assist with determination and/or condition of repair costs, process a claim, detect fraud, and/or train the system to better review future images. End-¶ [0074]: “Also, the module can be configured to apply these same learning techniques to image capture and/or augmentation that is used to initiate vehicle glass damage identification and/or repair determination. For example, the preparation of additional images from single images of damage and the comparison of those images to image rules. The image rules including but not limited to previously categorized images of window damage. [Also see Fig. 4D elements 72, 74: A.I. photo capture, classification and size measurement of damage; and Fig. 19 elements 509-510: photos captured and uploaded to A.I.; and ¶s [0048], [0074], [0075], [0094]]);
Receive, from a client device, data including one or more images of a glass of a window (Larson mid-¶ [0048]: The image(s) captured utilizing camera 18 can be then transferred or uploaded to processing circuitry 20, which includes a database 22 operably coupled to software 24 and hardware 26. ¶ [0074]: Module 34 is entitled "FNOL" (First Notice of Loss) can be filed using but not limited to a carrier web site, carrier app, or NCS interactive voice response system, for example. …a description of the damages is requested; appearance, size, and quantity…. Once the claim has been verified, the methods of the present disclosure can prompt the insured for required photos…. also the Machine Learning and Training module 36 can utilize images acquired to determine the presence of a repairable chip; the presence of a repairable crack; or the presence of a chip and/or crack that cannot be repaired and requires glass replacement);
input the received data including the one or more images of the glass of the window into
the trained computer vision model, wherein the computer vision model outputs characteristics of
damage of the glass of the window based upon the input, the characteristics of the damage
including (i) a type of the damage, and (ii) a size of the damage (Larson mid-¶ [0074]: … also the Machine Learning and Training module 36 [EN: computer vision model] can utilize images acquired to determine the presence of a repairable chip; the presence of a repairable crack; or the presence of a chip and/or crack that cannot be repaired and requires glass replacement…. Also, the module can be configured to apply these same learning techniques to image capture and/or augmentation that is used to initiate vehicle glass damage identification and/or repair determination. For example, the preparation of additional images from single images of damage and the comparison of those images to image rules. The image rules including but not limited to previously categorized images of window damage [EN: historical images and characteristics]. End-¶ [0075]: … computer vision can be used to prepare extracts of glass damage information that describes the morphology and structure of glass damage via tagging tasks that provide for labeling of specific features (size and shape) of a given image. Classification of a given image can be based on specific detection tasks that are tied to pixel-level precision for image detection. Detection is followed with segmentation into pre-defined glass damage buckets based on ROLAGS standards. ¶ [0094]: Referring next to FIG. 13, an example implementation of a machine learning relating to the images is shown, wherein images are received in step 300, and the damage classified in step 302 by the type of break at 304, the size of the break in 306, the number of breaks per image in 308, and the location of the breaks on the windshield itself in 310);
input the characteristics of the damage output by the computer vision model into a machine learning model that outputs a type of repair to fix the damage, the machine learning model trained based upon the historical characteristics and historical types of repair performed in response to the historical characteristics (Examiner interprets type of repair to include repairing or replacing the damaged windshield, consistent with Applicant specification ¶ [0032, 0071]. See Larson Table 1, referred to as a truth table, indicating type of repair determined, repair or replace, based on the size and type of the windshield damage. Larson mid-¶ [0094]: This machine learning is done first with data augmentation at 312 (example implementations are described with reference to FIGS. SE and 7B), and then the system fits received data to model in 314 and compared to the truth table as described, for example, in FIGS. 12A and 12B, at step 316, for a final disposition regarding fraud and/or replacement or repair of the windshield at 318. End-¶ [0074]: Also, the module can be configured to apply these same learning techniques to image capture and/or augmentation that is used to initiate vehicle glass damage identification and/or repair determination. For example, the preparation of additional images from single images of damage and the comparison of those images to image rules. The image rules including but not limited to previously categorized [EN: historical] images of window damage. End-¶ [0075]: Classification of a given image can be based on specific detection tasks that are tied to pixel-level
precision for image detection. Detection is followed with segmentation into pre-defined glass damage buckets [EN: historical characteristics / repair types] based on ROLAGS standards);
[..]
Although Larson teaches receiving data for a glass damage claim, determining the type and size of the damage, and determining how to address the damage, Larson does not specifically teach communicating the damage and repair assessment with a user.
However, Laskowski in analogous art of windshield damage repair teaches or suggests:
cause the client device to present the output type of repair to a user of the client device
(Laskowski ¶ [0018]: Detection of cracks or chips in the windshield can be used to increase the safety in the vehicle and increase convenience for the user, driver, or owner of a vehicle. The present disclosure includes techniques for automatically scheduling a windshield repair or replacement [EN: see Applicant specification ¶ [0032, 0071] for types of repair including repair or replacement] based on detected damage. In an example, the detection of damage to the windshield could notify the driver either through a Heads-Up Display (HUD), alternative vehicle display, or a separate device personally accessible to the user or owner of the vehicle).
Laskowski and Larson are found as analogous art of windshield damage repair. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Larson’s motor vehicle glass insurance claim system and method to have included Laskowski’s teachings around communicating the damage and repair assessment with a user. The benefit of these additional features would have provided additional convenience for the user/insured (Laskowski ¶ [0018]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Larson in view of Laskowski (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of windshield damage repair. In such combination each element would have merely performed same organizational and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Larson in view of Laskowski above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claim 2: Cancelled
Regarding claim 3: Larson / Laskowski teach all the limitations of claim 1 above.
Larson further teaches:
wherein the received data includes one or more images or video reels (Larson ¶ [0008]: the present disclosure also provides a non-transitory computer readable storing instruction that when executed by a processor, causes a computer system to perform the following method. The method can include: prompting a user for initial claim submission information; prompting the user for a plurality of images of portions of motor vehicle glass; performing image processing operations on each of the plurality of images to train the computer system, determine one or more of glass damage, glass type, and/or claim fraud; and one of submit or reject an insurance claim for motor vehicle glass repair or replacement).
Regarding claim 4: Larson / Laskowski teach all the limitations of claim 1 above.
Larson further teaches:
wherein the at least one processor is further programmed to identify the type of damage to the glass as being one of a long crack, a bull's eye crack, a star crack, or a half-moon crack (Larson ¶ [0092]: In accordance with example implementations, and with reference to FIGS. 11A-11E, example windshield chips are shown, with FIG. 11A representing a half moon chip of the size shown; FIG. 11B representing a star chip of the size shown; FIG. 11C representing a bullseye chip of the size shown; FIG. 11D showing a combo chip of the size shown, and FIG. 11E showing a batwing chip of the size shown; FIG. 11F showing a crack of the size shown).
Regarding claim 7: Larson / Laskowski teach all the limitations of claim 1 above.
Larson further teaches:
wherein in response to the at least one processor determining that the type of damage is a bull's eye crack and the size of the bull's eye crack is a second value in diameter (See Larson Fig. 11C: user uploaded image of bullseye chip with ruler for diameter reference), the at least one processor is further programmed to:
determine the type of repair to fix the damage by:
comparing the second value in diameter with a second threshold diameter value (See Larson Table 1: Type: Bullseye chip; Size: 1” or less in diameter);
in response to the second value in diameter exceeding the second threshold diameter value, determining that the type of repair is a replacement job (See Larson Table 1: Type: Bullseye chip; Size: 1” or less in diameter; “NO” column indicating diameter IS NOT 1” or less; result is “Replace”); and
in response to the second value in diameter being less than the second threshold diameter value, determining that the type of repair is a repair job (See Larson Table 1: Type: Bullseye chip; Size: 1” or less in diameter; “YES” column indicating diameter IS 1” or less; result is “Repair”).
Regarding claim 8: Larson / Laskowski teach all the limitations of claim 7 above.
Larson further teaches:
wherein the second threshold diameter value is about 2 centimeters [EN: 0.8 inches] in diameter (See Larson Table 1: Type: Bullseye chip; Size: 1” or less in diameter [EN: Examiner interprets 1” to be about 0.8 inches or 2 centimeters. Setting a nonzero limit for a threshold is well known in the art]).
Regarding claim 9: Larson / Laskowski teach all the limitations of claim 1 above.
Larson further teaches:
the type of damage is a star crack; the size or the dimension of the star crack is a third value in diameter (See Larson Fig. 11B: user uploaded image of star chip with ruler for diameter reference); and
the determining the type of repair to fix the damage comprises:
comparing the third value in diameter with a third threshold diameter value (See Larson Table 1: Type: Star chip; Size: 1” in diameter or less);
based upon the comparison indicating the third value in diameter exceeding the third threshold diameter value, determining the type of repair to be a replacement job (See Larson Table 1: Type: Star chip; Size: 1” in diameter or less; “NO” column indicating diameter IS NOT 1” or less; result is “Replace”); and
based upon the comparison indicating the third value in length being less than the third threshold length value, determining the type of repair to be a repair job (See Larson Table 1: Type: Star chip; Size: 1” in diameter or less; “NO” column indicating diameter IS NOT 1” or less; result is “Repair”).
Regarding claim 10: Larson / Laskowski teach all the limitations of claim 9 above.
Larson further teaches:
wherein the third threshold diameter value is about 3 centimeters [EN: 1.2 inches] (See Larson Table 1: Type: Star chip; Size: 1” in diameter or less [EN: Examiner interprets 1” to be about 1.2 inches or 3 centimeters. Setting a nonzero limit for a threshold is well known in the art]).
Regarding claim 11: Larson / Laskowski teach all the limitations of claim 1 above.
Larson further teaches:
the type of damage is a half-moon crack; the size or the dimension of the half-moon crack is a fourth value in diameter (See Larson Fig. 11A: user uploaded image of half moon chip with ruler for diameter reference); and
the determining the type of repair to fix the damage comprises:
comparing the fourth value in diameter with a fourth threshold diameter value (See Larson Table 1: Type: Halfmoon chip; Size: 1” or less in diameter);
based upon the comparison indicating the fourth value in diameter exceeding the fourth threshold diameter value, determining the type of repair to be a replacement job (See Larson Table 1: Type: Halfmoon chip; Size: 1” or less in diameter; “NO” column indicating diameter IS NOT 1” or less; result is “Replace”); and
based upon the comparison indicating the fourth value in length being less than the fourth threshold length value, determining the type of repair to be a repair job (See Larson Table 1: Type: Halfmoon chip; Size: 1” or less in diameter; “YES” column indicating diameter IS 1” or less; result is “Repair”).
Regarding claim 12: Larson / Laskowski teach all the limitations of claim 11 above.
Larson further teaches:
wherein the fourth threshold diameter value is about 2.5 centimeters [EN: 1 inch] (See Larson Table 1: Type: Halfmoon chip; Size: 1” or less in diameter [EN: Examiner interprets 1” to be about 1 inch or 2.5 centimeters. Setting a nonzero limit for a threshold is well known in the art]).
Regarding claim 13: Larson / Laskowski teach all the limitations of claim 1 above.
Larson further teaches:
the characteristics of the damage further includes a location of the damage, and the determining the type of repair further comprises determining the type of repair based upon the location of the damage (Larson ¶ [0099]: For example, additional details relating to the size of the chip, the location of the chip can be entered into the system and be part of the machine learning. This can provide for additional efficiency in the system and method. ¶ [0104]: At step 509, the user can be prompted to or provide a series of photos which are captured at 509, information [including location, see Fig. 19] is uploaded to the A.I. at 510 and then the method proceeds to the repair/replace decision).
Regarding claim 15: Cancelled
Regarding claim 16: Cancelled
Regarding claim 17: Larson / Laskowski teach all the limitations of claim 14 above.
Larson further teaches:
wherein the characteristics of the damage further includes a location of the damage (Larson ¶ [0099]: For example, additional details relating to the size of the chip, the location of the chip can be entered into the system and be part of the machine learning. This can provide for additional efficiency in the system and method. ¶ [0104]: At step 509, the user can be prompted to or provide a series of photos which are captured at 509, information [including location, see Fig. 19] is uploaded to the A.I. at 510 and then the method proceeds to the repair/replace decision).
Regarding claim 19: Larson / Laskowski teach all the limitations of claim 18 above.
Larson further teaches:
wherein the received data includes visual data, and the visual data is analyzed using a machine- learning model trained using historical claims data (Larson ¶ [0056]: As described in greater detail herein, devices 18 can be used to capture one or more images of damaged glass. The images are transmitted over a network connection to a server. The server can process the images to assess damage, obtain information to assist with determination and/or condition of repair costs, process a claim, detect fraud, and/or train the system to better review future images [EN: from historical images]).
Regarding claim 20: Larson / Laskowski teach all the limitations of claim 18 above.
Larson further teaches:
wherein the type of repair is determined further in accordance with a location of the damage (Larson ¶ [0099]: For example, additional details relating to the size of the chip, the location of the chip can be entered into the system and be part of the machine learning. This can provide for additional efficiency in the system and method. ¶ [0104]: At step 509, the user can be prompted to or provide a series of photos which are captured at 509, information [including location, see Fig. 19] is uploaded to the A.I. at 510 and then the method proceeds to the repair/replace decision).
Regarding claim 22: Larson / Laskowski teaches all the limitations of claim 1 above.
Larson further teaches:
wherein the at least one processor is further programmed to train the machine learning model based upon the historical characteristics and historical types of repair performed in response to the historical characteristics (Examiner interprets type of repair to include repairing or replacing the damaged windshield, consistent with Applicant specification ¶ [0032, 0071]. See Larson Table 1, referred to as a truth table, indicating type of repair determined, repair or replace, based on the size and type of the windshield damage. Larson mid-¶ [0094]: This machine learning is done first with data augmentation at 312 (example implementations are described with reference to FIGS. SE and 7B), and then the system fits received data to model in 314 and compared to the truth table as described, for example, in FIGS. 12A and 12B, at step 316, for a final disposition regarding fraud and/or replacement or repair of the windshield at 318. End-¶ [0074]: Also, the module can be configured to apply these same learning techniques to image capture and/or augmentation that is used to initiate vehicle glass damage identification and/or repair determination. For example, the preparation of additional images from single images of damage and the comparison of those images to image rules. The image rules including but not limited to previously categorized [EN: historical] images of window damage. End-¶ [0075]: Classification of a given image can be based on specific detection tasks that are tied to pixel-level
precision for image detection. Detection is followed with segmentation into pre-defined glass damage buckets [EN: historical characteristics / repair types] based on ROLAGS standards).
Regarding claim 23: Larson / Laskowski teaches all the limitations of claim 1 above.
Although Larson teaches receiving data for a glass damage claim, determining the type and size of the damage, and determining how to address the damage, Larson does not specifically teach communicating the damage and repair assessment with a user nor prompting the user for acceptance and facilitating scheduling the service appointment.
However, Laskowski in analogous art of windshield damage repair teaches or suggests:
wherein the at least one processor is further programmed to cause the client device to prompt the user to accept (a) the type of repair, and (b) a proposed scheduling of an appointment to fix the damage based upon the type of repair (Laskowski ¶ [0018]: Detection of cracks or chips in the windshield can be used to increase the safety in the vehicle and increase convenience for the user, driver, or owner of a vehicle. The present disclosure includes techniques for automatically scheduling a windshield repair or replacement [EN: see Applicant specification ¶ [0032, 0071] for types of repairs including repair or replacement] based on detected damage. In an example, the detection of damage to the windshield could notify the driver either through a Heads-Up Display (HUD), alternative vehicle display, or a separate device personally accessible to the user or owner of the vehicle. ¶ [0033]: The driver prompted service scheduler 206 may receive a signal from the driver information system 104 and in response may contact the user/driver to obtain input about scheduling a windshield service 112. In an example, the driver prompted service scheduler 206 may present the user/driver with the next available appointment with the windshield service 112 and request confirmation from the user/driver if they would like to accept or decline the appointment. In an example, the driver prompted service scheduler 206 may be communicating to the user/driver through SMS, an application, the cluster, or a CID. The driver prompted service scheduler 206 may request other information from the driver including a request for a price range, a request for a date range, a request for a brand, a request for a time of service range, or any other input for the scheduling of an appointment and ordering of a windshield).
Laskowski and Larson are found as analogous art of windshield damage repair. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Larson’s motor vehicle glass insurance claim system and method to have included Laskowski’s teachings around communicating the damage and repair assessment with a user and prompting the user for acceptance and facilitating scheduling the service appointment. The benefit of these additional features would have provided additional convenience for the user/insured (Laskowski ¶ [0018]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Larson in view of Laskowski (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of windshield damage repair. In such combination each element would have merely performed same organizational and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Larson in view of Laskowski above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
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Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over:
Larson / Laskowski, as applied above, in further view of
Emeka, Abonyi S. Analysis of Windshield Repair Technology. May-June 2018. International Journal of Trend in Scientific Research and Development. Volume 2 Issue 4. http://eprints.umsida.ac.id/12144/ ,
hereinafter Emeka. As per,
Regarding claim 5: Larson / Laskowski teaches all the limitations of claim 1 above.
Larson further teaches:
[..]
[..]
the at least one processor is further programmed to: determine the type of repair to fix the damage by:
in response to the first value in length exceeding the first threshold length value, determining that the type of repair is a replacement job (See Larson Table 1: damage type column; size threshold column; “NO” column indicating measurement IS NOT the threshold size or less; result is “Replace”); and
in response to the first value in length being less than the first threshold length value, determining that the type of repair is a repair job (See Larson Table 1: damage type column; size threshold column; “YES” column indicating measurement IS the threshold size or less; result is “Repair”).
Although Larson teaches evaluating replace or repair plans based on damage assessment of windshields including various damage types and sizes, Larson is not explicit about the scenario where the damage type is a long crack and comparing the threshold value to the long crack’s length.
However, Emeka in analogous art of windshield damage repair teaches or suggests:
wherein in response to the at least one processor determining that the type of damage is a long crack and the size of the long crack is a first value in length (Emeka ¶ 3.3 ii: Long crack – A crack on the windshield of more than 6 inches in length),
the at least one processor is further programmed to: determine the type of repair to fix the damage by: comparing the first value in length with a first threshold length value (Emeka ¶ 3.3 ii: Long crack – A crack on the windshield of more than 6 inches in length);
Emeka, Laskowski and Larson are found as analogous art of windshield damage repair. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Larson / Laskowski’s motor vehicle glass insurance claim system and method to have included Emeka’s teachings around the damage type being a long crack and comparing the threshold value to the long crack’s length. The benefit of these additional features would have added to the repertoire of assessment procedures and repair/replace options for additional types of damage. The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Larson in view of Laskowski and Emeka (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of windshield damage repair. In such combination each element would have merely performed same organizational and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Larson in view of Laskowski and Emeka above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claim 6: Larson / Laskowski / Emeka teaches all the limitations of claim 5 above.
Although Larson teaches teaches evaluating replace or repair plans based on damage assessment of windshields including various damage types and sizes, Larson is not explicit about the scenario where the damage type is a long crack and comparing the threshold value of 6 inches to the long crack’s length.
However, Emeka in analogous art of windshield damage repair teaches or suggests:
wherein the first threshold length value is about 15 centimeters [EN: 6 inches] in length (Emeka ¶ 3.3 ii: Long crack – A crack on the windshield of more than 6 inches in length).
Rationales to have modified / combined Larson / Laskowski / Emeka are above and reincorporated.
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Conclusion
The following art is made of record and considered pertinent to Applicant’s disclosure:
Hansen et al. WO 2017194950 A1, Break analysis apparatus and method.
Hart et al. US 20070136106 A1, Method and system of managing and administering automotive glass repairs and replacements.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to REED M. BOND whose telephone number is (571) 270-0585. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached at (571) 270-5396. 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.
/REED M. BOND/Examiner, Art Unit 3624
April 15, 2026
/HAMZEH OBAID/Primary Examiner, Art Unit 3624
1 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”.
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