DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The amendment filed on June 10, 2026 has been entered. The following has occurred: Claims 1, 8, 9, 16, and 17 have been amended.
Claims 1-23 are pending.
Response to Amendment
35 U.S.C. 101 rejection has been maintained in light of the amendment.
35 U.S.C. 103 rejection has been withdrawn, however, new 35 U.S.C. 103 rejection has been added in light of the amendment.
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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture or composition of matter? (MPEP 2106.03)
In the present application, claims 1-8 are directed to a system (i.e., a machine), claims 9-16 are directed to a computer product (i.e. an article of manufacture), claims 17-23 are directed to a method (i.e., a process). Thus, the eligibility analysis proceeds to Step 2A. prong one.
Step 2A. prong one: Does the claim recite an abstract idea, law of nature, or natural phenomenon? (MPEP 2106.04)
While claims 1, 9, and 17, are directed to different categories, the language and scope are substantially the same and have been addressed together below.
The abstract idea recited in claims 1, 9, and 17, is
training one or more machine learning models using historical examples of images of damaged vehicles and corresponding vehicle repair estimate lines;
a user to generate one or more vehicle repair estimate lines for repairing a damaged vehicle and the user to request an automated review of the one or more lines, and present additional repair guidance to the user;
generating one or more first vehicle repair estimate lines for repairing the damaged vehicle, adding the generated one or more first vehicle repair estimate lines to a vehicle repair estimate data structure, and presenting a first view of the vehicle repair estimate data structure;
updating to (the) present additional guidance based on at least one of suggested parts selected by the user or suggested parts generated;
responsive to request the automated review, performing automated review of the generated one or more first vehicle repair estimate lines in the vehicle repair estimate data structure, wherein the performing the automated review comprises:
obtaining one or more input data including one or more images of the damaged vehicle and associated repair-related data,
generating a first output comprising one or more second vehicle repair estimate lines for repairing the damaged vehicle,
adding the one or more second vehicle repair estimate lines to the vehicle repair estimate data structure, and
presenting a second view of the vehicle repair estimate data structure, including one or more of the one or more second vehicle repair estimate lines;
determining based on the one or more images whether the damaged vehicle include prior damage unrelated to a current damage event;
responsive to determining the prior damage, updating to present a notification identifying the prior damage and enabling user selection to at least one of exclude the prior damage from the vehicle repair estimate damage data structure or generate a separate estimate for the prior damage; and
commit the vehicle repair estimate, generating a vehicle repair estimation document based on the vehicle repair estimate data structure.
(Broadest reasonable interpretation: “automated” or “automatically” does not mean without human interaction. Examiner asserts a process may be automatic even though a human initiates or may interrupt to the process. The term “automatically” or “automated” can be construed to mean “once initiated by a human, the function is performed by a machine, without the need for manually performing the function.” Collegenet, Inc. v. Applyyourself, Inc. (CAFC, 04-1202,-1222,-1251, 8/2/2005).)
The claimed invention is directed to an abstract idea of generating a vehicle repair estimate. This is a method of organizing human activity of fundamental economic practice, that has been performed by insurance adjusters and repair technicians, involving the mental steps of identifying primary damage, using experience for evaluation of related consequential damage, and compiling a final list.
That is, under the broadest reasonable interpretation, without the recitation of additional elements, the limitations above suggest a process similar to inputting and collecting information (e.g., generating, adding, obtaining, and providing limitations), analyzing the information (e.g., perform review; training machine learning models; determining prior damage unrelated to current damage event), and presenting information. Because the limitations above closely follow the steps of collecting information and analyzing the collected information, and the steps involved human judgements, observations, and evaluations that can be practically or reasonably performed in the human mind, the claims recite an abstract idea consistent with the “mental processes” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(III).
Additionally and alternatively, the same claim limitations above recite a fundamental economic practice long prevalent in our system of commerce in the form of vehicle repair services. Under the broadest reasonable interpretation, other than the additional elements of computer components, the limitations involve a process of collecting receiving manual data entry (“first … line”), training a mathematical (machine learning) model with the collected information, using mathematic (machine learning) model to analyze data, determining prior damage, estimating vehicle repair, suggesting related items (“second… line”), storing data, and presenting it. These steps recite commercial interaction for a business practice of providing a service of vehicle repair estimation, the claims recite an abstract idea consistent with the “certain methods of organizing human activity” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(II).
Accordingly, the above-mentioned limitations are considered as a single abstract idea, therefore, the claims recite an abstract idea and the analysis proceeds to Step 2A. prong two.
Step 2A. prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? (MPEP 2106.04)
This judicial exception is not integrated into a practical application because the additional elements merely add instructions to apply the abstract idea to a computer.
While claims 1, 9, and 17, are directed to different categories, the claims recite similar additional elements.
The additional elements considered include:
“the system comprising: a hardware processor; and a non-transitory machine-readable storage medium encoded with instructions executable by the hardware processor to cause the system to perform operations comprising: generating a user interface comprising one or more first active display elements operable by a user”, “training one or more machine learning (ML) models using historical examples of images of damaged vehicles and corresponding vehicle repair estimate lines;” “by the one or more ML models,” “preprocessing the one or more input data by transforming the one or more input data from a plurality of different file types into a unified digital format and extracting structured information from the one or more input data using at least one optical character recognition (OCR) or natural language processing (NLP) techniques;” “processing the preprocessed input data using the one or more trained ML models, the one or more ML models including a feature extraction layer configured to extract feature representation from the preprocessed input data;” “using the one or more ML models,” “a second active display element operable by the user”, “responsive to operation of one or more first active display elements,” “in the user interface;” “responsive to operation of the second active display element:” “after presenting a second view of the vehicle repair estimate data structure and responsive to operation of a third active display element in the user interface, the third active display element operable by the user to"; “one or more guidance display elements configured to”; “the user interface to present … via the one or more guidance display element”;
In particular, the claim only recites the above-mentioned additional elements of computer components to generate, add, request, obtain, provide, and present information. The computer in the steps is recited at a high-level of generality (i.e., Applicant’s Specification at least at paragraphs [0062]-[0077] describing generic computer components that may be any form of hardware, or combination of hardware and software performing a generic computer functions. In para. [0044] states “disclosed technologies may include the use of one or more trained machine learning models at one or more points in the described processes. Any machine learning models may be used.” describing any generic machine learning models) such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
That is, the function of limitations [A]-[L] are steps of 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 as discussed in MPEP 2106.05(f). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer. Accordingly, even in combination, these additional element(s) do not integrate the abstract idea into a practical application because they do not improve a computer or other technology, do not transform a particular article, do not recite more than a general link to a computer, and do not invoke the computer in any meaningful way; the general computer is effectively part of the preamble instruction to “apply” the exception by the computer. Therefore, the claims are directed to an abstract idea and the analysis proceeds to Step 2B.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? (MPEP 2106.05)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the bold portions of the limitations recited above, were all considered to be an abstract idea in Step2A-Prong Two. The additional elements and analysis of Step2A-Prong two is carried over. For the same reason, these elements are not sufficient to provide an inventive concept. Applicant has merely recited elements that instruct the user to apply the abstract idea to a computer or other machinery. When considered individually and in combination the conclusion, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the above-mentioned limitations [A]-[L] amount to no more than mere instructions to apply the function of the limitations to the exception using generic computer component, as discussed in MPEP 2106.05(f). The claim as a whole merely describes how to generally “apply” the concept for generating a vehicle repair estimate. Thus, viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. For these reasons there is no inventive concept in the claims and thus are ineligible.
As for dependent claims 2-8, 10-16, and 18-23, these claims recite limitations that further define the abstract idea noted in claims 1, 9, and 17. The claims further recite additional abstract steps of modifying, checking, requesting, providing, adding, presenting, and ordering information, which do not change the abstract idea of the independent claim. The claims recite the additional element of computer components at a high level of generality (i.e. as a generic computer system and interface performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component, as discussed in MPEP 2106.05(f). Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
In summary, the dependent claims considered both individually and as ordered combination do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. Therefore, claims 1-23 are rejected under 35 U.S.C. 101.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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 1-23 are rejected under 35 U.S.C. 103 as being unpatentable over Nelson et al. (US 10922726 B1), hereinafter “Nelson,” in view of Chong et al. (US 20220058579 A1), hereinafter “Chong,” in view of Taliwal et al. (US 20170293894 A1, previously presented under “Relevant Prior Art Not Relied Upon”), hereinafter, “Taliwal,” and further in view of Franke et al. (US 20170148102 A1), hereinafter, “Franke.”
Claims 1, 9, and 17, Nelson discloses a system and method for automatically guiding completion of a vehicle repair estimation document (Claim 1, Fig. 1, and Col 1 Ln. 6-7 system; abstract and Claim 9, method), the system comprising:
a hardware processor (Col. 2 Ln. 59-63, processor); and
a non-transitory machine-readable storage medium encoded with instructions executable by the hardware processor to cause the system to perform operations comprising (Col. 1 Ln. 56, memories storing a plurality of routines; Col. 36 Ln. 32-53):
training one or more machine learning (ML) models using historical examples of images of damaged vehicles and corresponding vehicle repair estimate lines (Col. 9 Ln. 45 – Col. 10 Ln. 52 and Col. 41 Ln. 1-8, disclosing training… a machine learning model… based on historical data associated with repairing a plurality of damaged vehicles, and the historical data indicating… a respective set of vehicle parts and a respective set of historical labor operations that were used to repair the each damage vehicle. In Col. 36 Ln. 12-44 and Col. 42 Ln. 35-39 disclosing the historical data includes images);
generating a user interface comprising one or more first active display elements operable by a user to generate one or more vehicle repair estimate lines for repairing a damaged vehicle (Claim 1(e) and Col. 41, Ln. 41-43, Nelson discloses obtaining a user-generated modification via the user interface, which is for “generating one or more first vehicle repair estimate lines.”) and operable by the user to request an automated review of the one or more lines (Claim 1(f) and Col.41 Ln. 44-48, disclosing user’s manual modification itself subsequently initiates another iteration of the automated loop);
responsive to operation of the one or more first active display elements, automatically generating, by the one or more ML models, one or more first vehicle repair estimate lines for repairing the damaged vehicle (Col. 41, Ln. 41-43), adding the generated one or more first vehicle repair estimate lines to a vehicle repair estimate data structure, and presenting a first view of the vehicle repair estimate data structure in the user interface (Col. 41, Ln. 44-48, disclosing after a user modification is obtained, the system applies the user-generated modification to the current system modified draft estimate, thereby generating a user-modified draft estimate. The updated estimate is displayed. Col. 34 Ln. 21-24);
updating the user interface to present additional guidance via the one or more guidance display element based on at least one of suggested parts selected by the user or suggested parts generated by the one or more ML models (Col. 29 Ln. 43-65 and Claim 13 discloses guidance annotations can be interactive guidance annotations that allows a user to select modifications and the system applies only user-selected modifications to update the estimate);
responsive to operation to request the automated review, performing the automated review of the generated one or more first vehicle repair estimate lines in the vehicle repair estimate data structure (claim 1 recites, “(c) execute one or more iterations of an automated loop with respect to the initial draft estimate, each iteration of the automated loop including: analyzing, by utilizing the trained machine learning model, a current draft estimate to determine, for at least one part indicated in the current draft estimate, whether or not any system-generated modifications are to be made to the current draft estimate,” Further in Col. 25 Ln. 37-44: “The user interface 405 may present an interactive display of the current system-revised draft estimate for user review and optional user-generated approval or modification. If the user approves of the current draft estimate, e.g., by activating a corresponding user control (as denoted in FIG. 4 by the arrow (f)), the approved estimate may be stored in a system data store or database 418 and/or may be transmitted to other computing systems”. Col. 27 Ln. 9-13: “Viewing the execution of the larger loop from the perspective of the user interface 405, each time a single or individual user-modification to a current draft estimate is entered by the user (e.g., via (f)) and provided to the machine portion 408 of the system (e.g., via (g) or (h)), the machine portion 408 of the system 400 operates on the single or individual user-modification to generate a resulting updated draft estimate that is presented at the user interface 405 (e.g., via (d)).” Col. 28 LN. 13-17: “system 400, the intelligent interceptor engine 410 generates one or more sets of guidance annotations to aid a user in revising, refining, and/or completing a current draft estimate that is displayed on the user interface 405.” Col. 33 Ln. 1-17, disclosing current draft estimate are automatically performed. The analyzing current draft estimate is representative of performing automated review of the generated one or more first vehicle repair estimate lines in the vehicle repair estimate data structure),
wherein the performing the automated review comprises:
obtaining one or more input data including one or more images of the damaged vehicle and associated repair-related data (Col. 5 Ln. 49-67; Col. 36 Ln. 12-25 discloses the input data includes images and other types of data that correspond to the damaged vehicle, such as telematics, incident facts, a vehicle build sheet, sensor data, diagnostics data, accident reports);
processing the input data using the one or more images to the one or more trained machine learning (ML) models (Claim 8, Col. 42 Ln. 35-39; col. 17 Ln. 39-52 disclosing obtaining one or more images of a damage vehicle and “image processing component that extracts image attributes from one or more images of a damage vehicle.”),
generating, by the one or more ML models, a first output comprising one or more second vehicle repair estimate lines for repairing the damaged vehicle (Nelson, Col. 41 LN. 13-25, discloses the system’s intelligent interceptor engine utilizes a trained machine learning model to analyze the current draft estimate and determine if “any system-generated modifications are to be made… the any system-generated modifications including at least one of an addition of a field corresponding to another part or an addition of a field corresponding to another labor operation”. The system-generated additions are “second vehicle repair estimate lines”);
adding the one or more second vehicle repair estimate lines to the vehicle repair estimate data structure; and presenting a second view of the vehicle repair estimate data structure in the user interface, including one or more of the one or more second vehicle repair estimate lines (Col. 41 Ln. 26-34, “when at least one system-generated modification is to be made to the current draft estimate, applying the at least one system-generated modification to the current draft estimate… thereby updating the current draft estimate”; and Col. 41 Ln. 38-40, “display the current system-modified draft estimate at the user interface”);
after presenting a second view of the vehicle repair estimate data structure and responsive to operation of a third active display element in the user interface, the third active display element operable by the user to commit the vehicle repair estimate, generating a vehicle repair estimation document based on the vehicle repair estimate data structure (Col. 25 Ln. 39-44, disclosing after the iterative refinement, “If the user approves the current draft estimate, e.g., by activating a corresponding user control… the approved estimate may be stored in a system data store or database 418 and/or may be transmitted to other computer systems”. The approval via user control is representative to operating a “third active display element” to generate the final document).
Nelson does not explicitly disclose a separate “second active display element” operable by the user to request an automated review, however, it is implying an implicit trigger in Col. 41 Ln. 44-48 for user action to initiate another iteration of the automated loop with respect to the user-modified draft estimate.
Specifically, Nelson fails to explicitly disclose (italic emphasis) the limitations:
a second active display element operable by the user to request an automated review of the one or more lines, and one or more guidance display elements configured to present additional repair guidance to the user;
preprocessing the one or more input data by transforming the one or more input data from a plurality of different file types into a unified digital format and extracting structured information from the one or more input data using at least one optical character recognition (OCR) or natural language processing (NLP) techniques;
the one or more ML models including a feature extraction layer configured to extract feature representation from the preprocessed input data;
determining, based on the one or more images using the one or more ML models, whether the damaged vehicle includes prior damage unrelated to a current damage event;
responsive to determining the prior damage, updating the user interface to present a notification identifying the prior damage and enabling user selection to at least one of exclude the prior damage from the vehicle repair estimate data structure or generate a separate estimate for the prior damage;
However, Chong teaches a system for vehicle repair workflow automation that includes a user-initiated review function, specifically teaches, a second active display element operable by the user to request an automated review of the one or more lines, and one or more guidance display elements configured to present additional repair guidance to the user (para. [0040] “write estimate button 514” allowing a user using user-operable buttons to trigger system function, for an NLP machine learning model used to review specific vehicle repair procedures contained in the vehicle repair estimate and provide accuracy score, para. [0044]).
preprocessing the one or more input data by transforming the one or more input data from a plurality of different file types into a unified digital format and extracting structured information from the one or more input data using at least one optical character recognition (OCR) or natural language processing (NLP) techniques (Chong, Abstract and [0035], [0044] teaches applying Natural Language Processing (NLP) to extract and process vehicle repair estimate data).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the system and method of Nelson for iterative augmentation system in vehicle repair estimation to include the feature of a second active display element providing user trigger function as taught by Chong for the motivation of providing a more user friend and controllable estimation system, which improve user experience and providing more explicit control in the process of automated AI augmented estimation. Further, the claimed invention is merely a combination of old elements in a similar vehicle repair estimation field of endeavor. In such combination each element merely would have performed the same vehicle repair estimation related function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Chong, the results of the combination were predictable (See MPEP 2143 A).
The combination of Nelson and Chong fails to expressly teach,
the one or more ML models including a feature extraction layer configured to extract feature representation from the preprocessed input data;
determining, based on the one or more images using the one or more ML models, whether the damaged vehicle includes prior damage unrelated to a current damage event;
responsive to determining the prior damage, updating the user interface to present a notification identifying the prior damage and enabling user selection to at least one of exclude the prior damage from the vehicle repair estimate data structure or generate a separate estimate for the prior damage;
However, Taliwal is in similar field of vehicle repair estimation which specifically teaches (italic emphasis),
the one or more ML models including a feature extraction layer configured to extract feature representation from the preprocessed input data (para. [0145] and [0147] teaches processing vehicle damage images using Convolutional Neural Network (CNN) to extract features from the layers of neurons or feature maps, also known as convolutional layers (i.e., feature extraction layer));
determining, based on the one or more images using the one or more ML models, whether the damaged vehicle includes prior damage unrelated to a current damage event (para. [0036] teaches the system determines the current level of damages and compares it with the level of damage prior to filing of the current claim, as determined using image processing of prior images of the vehicle. Claim 24 claims comparing the current damage with a state of the vehicle prior to the current damage);
responsive to determining the prior damage, updating the user interface to present a notification identifying the prior damage (para. [0130], [0131] teaches a user interface for an adjuster to review the damage assessment and make decisions).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the system and method of Nelson for iterative augmentation system in vehicle repair estimation to include the feature of using CNN with NLP for object recognition of feature for vehicle damage and identifying prior damages for the motivation of providing an improved damage estimation system and method with better accuracy and speed of convergence for solution (para. [0121]).
The combination fails to expressly teach, enabling user selection to at least one of exclude the prior damage from the vehicle repair estimate data structure or generate a separate estimate for the prior damage.
Franke in similar field of vehicle damage and repair estimation system and method, specifically teaches enabling user selection to at least one of exclude the prior damage from the vehicle repair estimate data structure or generate a separate estimate for the prior damage (Franke, para. [0167]-[0168] teaches tracking damages occurred that a new renter/driver is not responsible for preexisting damages and the system can filter predetermined damage. In para. [0061] teaches user interface provides tool for a user to manually approve or disapprove specific repair item found.
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the system and method of Nelson for iterative augmentation system in vehicle repair estimation to include the teaching of filtering preexisiting damage by presenting the list on the UI and allowing the user to use the approval/disapproval tool to explicitly exclude the prior damage from the current estimate for providing an improved system and method for objectively evaluating damage to an item and automating and streamlining multi-party repair procedures (para. [0003]-[0004]).
Claims 2, 10, and 18, the combination of Nelson, Chong, Taliwal, and Franke make obvious of the system of claim 1, the one or more non-transitory machine-readable storage media of claim 9, and the method of claim 17. Nelson further discloses,
the user interface comprises a fourth active display element operable by the user to modify the one or more second vehicle repair estimate lines prior to committing the vehicle repair estimate; the user interface comprises a fifth active display element operable by the user to add one or more third vehicle repair estimate lines prior to committing the vehicle repair estimate (Col. 34 Ln. 8-18; and Col. 41 Ln. 41-48 disclosing “user-generated modification for modifying draft estimate; and Col.25 Ln. 52-67 which disclosing the ability for a user to modify existing line and add new lines); and
the user interface comprises a sixth active display element operable by the user to check the vehicle repair estimate using compliance rules prior to committing the vehicle repair estimate (Col. 13 Ln. 5-14; Col. 29 Ln. 59-65 discloses providing guidance annotations to the user including information may inform a user selection of various values, configuration parameter limits, and regulations, etc. which is teaching the access to compliance rules (i.e., regulations)).
Claims 3, 11, and 19, the combination of Nelson, Chong, Taliwal, and Franke make obvious of the system of claim 1, the one or more non-transitory machine-readable storage media of claim 9, and the method of claim 17. Nelson further discloses,
the user interface comprises a fourth active display element operable by the user to request automated generation of one or more third vehicle repair estimate lines for repairing the damaged vehicle (Claim 1(f) and Col.41 Ln. 44-48 “subsequently initiate another iteration of the automated loop with respect to the user-modified draft estimate” is teaching the AI process of automated loop which is intended to be run more than once. As established in the rejection of claim 1, the combination of Nelson and Chong renders obvious to use an active display element (i.e., button) to trigger the automated loop. It would have been obvious to ordinary skilled in the art for the same trigger mechanism to be used to initiate the subsequent iterations of the automated loop) and
responsive to operation of the fourth active display element, the operations further comprise: providing the one or more images to the one or more trained ML models, wherein responsive to the one or more images the one or more trained ML models provide second output comprising the one or more third vehicle repair estimate lines (Claim 1(c) and Col. 41 Ln. 16-25, “draft estimate to determine, for at least one part indicated in the current draft estimate, whether or not any system-generated modifications are to be made to the current draft estimate, the any system-generated modifications including at least one of an addition of a field corresponding to another part or an addition of a field corresponding to another labor operation;” Because Nelson explicitly teaches initiating another iteration of this exact loop, it directly teaches re-running the AL process to generate more system-generated modifications. The term “third vehicle repair estimate lines” is merely name given to the output of the second iteration of the automated loop), and
adding the one or more third vehicle repair estimate lines to the vehicle repair estimate data structure (Col. 41 Ln. 26-34, “when at least one system-generated modification is to be made to the current draft estimate, applying the at least one system-generated modification to the current draft estimate and causing one or more values of the current draft estimated to be updated… thereby updating the current draft estimate” This step is performed in every iteration, the new system-generated modifications (third lines) are applied and updated the estimate data structure); and
the operations further comprise presenting a third view of the vehicle repair estimate data structure in the user interface, including one or more of the one or more third vehicle repair estimate lines (Col. 41 Ln. 38-40, “display the current system-modified draft estimate at the user interface” This display occurs after each iterative cycle of modification and re-estimation).
Claims 4, 12, and 20, the combination of Nelson, Chong, Taliwal, and Franke make obvious of the system of claim 1, the one or more non-transitory machine-readable storage media of claim 9, and the method of claim 17. Nelson further discloses,
providing the vehicle repair estimation document to a claims adjuster (Col.1 Ln. 15-55 and Col. 8 Ln. 64-67 disclosing the computer-assisted techniques for generating estimates for vehicle repair for insurance adjuster or claims handler).
Claims 5, 13, and 21, the combination of Nelson, Chong, Taliwal, and Franke make obvious of the system of claim 1, the one or more non-transitory machine-readable storage media of claim 9, and the method of claim 17. Nelson further discloses,
the first output of the one or more trained ML models comprises relevance values for the one or more second vehicle repair estimate lines (Col. 30 Ln. 29-36 disclosing confidence level which is claimed “relevance value.” The scores generated by the model indicates the likelihood or correlation of a particular suggested repair line being necessary); and
the operations further comprise adding the relevance values to the vehicle repair estimate data structure in association with the one or more second vehicle repair estimate lines (Col. 30 Ln. 38-44, states “output of the model may be presented as guidance annotations” and provides examples “if a system configuration indicates a limit of the first confidence level, the guidance annotations may suggest that the user may want to including parts M and P in the draft estimate (and may not suggest including part Q)” the system perform this logic by compare the confidence level of a potential repair line to a configured limit and decide whether to present it as a suggestion, the system access to the confidence level value and its association with the specific repair line (e.g., Part M, P, or Q). , and
ordering the one or more second vehicle repair estimate lines in the second view of the vehicle repair estimate data structure in the user interface according to the relevance values (Col. 30 Ln. 29-44 describes the generation of a set of suggested repair lines (the guidance annotations) and the generation of scores for each of those suggestions. It would have been obvious for one ordinary skilled in the art to sort these confidence levels in any logical manner order for organized list to be easily identified for users).
Claims 6, 14, and 22, the combination of Nelson, Chong, Taliwal, and Franke make obvious of the system of claim 5, the one or more non-transitory machine-readable storage media of claim 13, and the method of claim 21. Nelson further discloses,
wherein the one or more trained ML models are further configured to: determine a point of impact on the damaged vehicle based on the one or more images of the damaged vehicle (Col. 9 Ln. 25-41 and Col. 40 ln. 63 – Col. 41 Ln. 5. disclosing training machine learning model to determine for vehicle part or labor operation that is more closely correlated to the repairing damaged vehicles. One ordinary skill in the art of vehicle damage appraisal would immediately understand that in the context of collision damage, the primary casual factor for damage correlation is the physical point of impact. The location and severity of the initial impact directly dictates which adjacent and related parts are likely to be damaged. Therefore, to build an effective model for determining correlated repairs. In Col. 5 Ln. 50- 62, teaches the system has access to the telematics data to make the above-mentioned determination); and
determine the relevance values based on the point of impact (Col. 30 Ln. 29-36 disclosing confidence level which is claimed “relevance value.” Col. 30 Ln. 29-44 describes the generation of a set of suggested repair lines (the guidance annotations) and the generation of scores for each of those suggestions. It would have been obvious for one ordinary skilled in the art to sort these confidence levels in any logical manner order for organized list to be easily identified for users
Claims 7, 15, and 23, the combination of Nelson, Chong, Taliwal, and Franke make obvious of the system of claim 1, the one or more non-transitory machine-readable storage media of claim 9, and the method of claim 17. Nelson further discloses,
the first output of the one or more trained ML models comprises damage severity values for the one or more second vehicle repair estimate lines (the “damage severity” is a qualitative concept that is quantified by metrics of cost to repair, time required for the repair, and type of labor involved. Logically, a more severe damage request more time and cost more. In Nelson Col. 9 Ln. 49- Col. 15 takes in consideration of damage severity value for the training of ML models. In Col. 42 Ln. 28-34 discloses the vehicle repair estimate includes part cost, labor operation costs, and labor operation times which are directly quantitative measures of damage severity result from the use of trained ML models); and
the operations further comprise presenting, in the second view of the vehicle repair estimate data structure, only those of the one or more second lines having damage severity values that exceed a damage severity threshold (Col. 41 Ln. 36-43, disclosing presenting generated list of quantitative values. It would have been obvious to a person of ordinary skill in the art to display the generated list of quantitative value for cost and time of the damages severity values as disclosed in Col. 41 Ln. 36-43 for the predictable result of more manageable and efficient user interface of presenting information and including the feature of filtering the values by defined threshold).
Claims 8 and 16, the combination of Nelson, Chong, Taliwal, and Franke make obvious of the system of claim 1, the one or more non-transitory machine-readable storage media of claim 9. Nelson further discloses,
obtaining one or more training data sets comprising the historical images of damaged vehicles and corresponding vehicle repair estimate lines; and training the one or more trained machine learning models using the training data set (Col. 9 Ln. 25-41 and Col. 40 ln. 63 – Col. 41 Ln. 5. disclosing training machine learning model).
Response to Remarks
35 U.S.C. 101 Rejection:
No specific argument was provided. The rejection has been maintained for the analysis provided in the Office Action above.
35 U.S.C. 103 Rejection:
The Examiner asserts that the Applicant’s arguments are directed towards amended claim limitations and are, therefore, considered moot. However, the Examiner has responded to the amended amendments, which the arguments are directed to, in the rejection above, thereby addressing the applicant’s arguments. New references Taliwal and Franke have been introduced to teach the amended limitations.
Relevant Prior Art Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. The additional cited art, including but not limited to the excerpts below, further establishes the state of the art at the time of Applicant’s invention and shows the following was known:
Lambert et al. (US 20200349370 A1) is directed to a system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repairs of a vehicle includes receiving one or more images of the vehicle from a policyholder. Lambert teaches severity based on damage.
K. Thonglek, N. Urailertprasert, P. Pattiyathanee and C. Chantrapornchai, "IVAA: Intelligent Vehicle Accident Analysis System," 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE), Chonburi, Thailand, 2019, pp. 85-90, doi: 10.1109/JCSSE.2019.8864186. Teaches an intelligent vehicle accidence analysis system provides an artificial intelligence as a service that can recognize the damaged vehicle part and the severity level facilitating the insurance company's claiming process.
Gastineau et al. (US 20210103817 A1) is directed to a method, non-transitory computer readable medium, and apparatus that improves automated damage appraisal includes analyzing one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output and which has stored knowledge data encoded from one or more stored property damage images to identify which area of the property has damage. Damage data on an extent of the damage in the identified area of the property is determined using the deep neural network which has stored knowledge data encoded from one or more stored property damage images. The identified damaged part and may be used to determine one or more adjacent parts based on the vehicle information and the repair operation type.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WENREN CHEN whose telephone number is (571)272-5208. The examiner can normally be reached Monday - Friday 10AM - 6PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan C Uber can be reached on (571) 270-3923. 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.
/WENREN CHEN/Primary Examiner, Art Unit 3626
Prosecution Insights