DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is a non-final Office Action on the merits. Claims 1-20 are currently pending and are addressed below.
Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Response to Argument
Applicant's amendments and arguments with respect to the rejection of claims 2-3, 5-6, 9-10, 12-13, 16-17 and 19-20 under 35 USC 112(b) as set forth in the office action of 11 September 2025 have been considered and are persuasive. Therefore, the rejection of claims 2-3, 5-6, 9-10, 12-13, 16-17 and 19-20 under 35 USC 112(b) as set forth in the office action of 11 September 2025 has been withdrawn.
Applicant’s amendments and/or arguments with respect to the rejection of Claims 1-20 under 35 USC 101 as set forth in the office action of 11 September 2025 have been considered and are NOT persuasive. Specifically, Applicant argues:
Claims 1-20 stand rejected under 35 U.S.C. § 101 as allegedly being directed to an
abstract idea. The Applicant respectfully disagrees and traverses the rejection. However, and without conceding the propriety of the rejection, amendments have been made herein that are thought to fully address the reasons for the rejection or otherwise render the rejection moot. For at least these reasons, the Applicant requests withdrawal of the rejection.
The Examiners Response:
Examiner has carefully considered Applicant’s amendments and arguments and respectfully disagrees. Regarding the claimed invention, the claims have a system that obtains DTC information and makes determinations to see if DTC are related to a collision even and removing a DTC from a repair estimate based on the determination. The claims can be performed in the human mind as they merely involve taking collected DTC information to make determinations on the connection of DTC to collision events, furthermore, the inclusion of a computer/processor does not integrate the abstract idea into a patent eligible invention, See Alice Corp. Pty. Ltd. v. CLS Bank Int'!, 573 U.S. at 223 ("[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention."). The device(s) and processor(s) are recited at a high level of generality and merely automates the steps. Furthermore, the addition of the “machine learning model” as an additional element is not sufficient to claim a practical application or amount to significantly more than a judicial exception as the limitation represents no more than mere instructions to apply the judicial exception on a computer and it can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers. As such, even in combination, these additional elements, under broadest reasonable interpretation, do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea.
Applicant’s amendments and/or arguments with respect to the rejection of Claims 1-20 under 35 USC 102 as set forth in the office action of 11 September 2025 have been considered but are moot because the new ground(s) of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a system, claim 8 is directed to one or more non-transitory computer-readable media and, claim 15 is directed to a method. Therefore, claims 1, 8 and 15 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. The other analogous claims 8 and 15 are rejected for the same reasons as the representative claim 1 as discussed here. Claim 1 recites:
A system, comprising:
one or more hardware processors;
and one or more non-transitory machine-readable storage media encoded with instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising:
receiving a diagnostic trouble code (DTC), the DTC specifying diagnostic information related to a component in a vehicle damaged in a collision event, the diagnostic information comprising one or more data inputs generated by the component;
determining, using a machine learning model trained on similar historical repair data that includes categorized DTCs pertaining to collision events, whether the DTC is definitely unrelated to the collision event based on the one or more data inputs;
in response to the DTC being determined not to be definitely unrelated to the collision event, determining, using the machine learning model, whether the DTC is related to the collision event by comparing a location of the component to a location of a point of impact associated with the collision event;
and removing the DTC from a repair estimate for the vehicle in response to the DTC being determined to be definitely unrelated to the collision event or not related to the collision event.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “determining …” and “removing …” all the various data in the context of this claim encompasses a person looking at data collected (received, detected, etc.) and forming a simple judgement (determination, analysis, comparison, etc.) either mentally or using a pen and paper. Accordingly, the claim recites at least one abstract idea. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A system, comprising:
one or more hardware processors;
and one or more non-transitory machine-readable storage media encoded with instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising:
receiving a diagnostic trouble code (DTC), the DTC specifying diagnostic information related to a component in a vehicle damaged in a collision event, the diagnostic information comprising one or more data inputs generated by the component;
determining, using a machine learning model trained on similar historical repair data that includes categorized DTCs pertaining to collision events, whether the DTC is definitely unrelated to the collision event based on the one or more data inputs;
in response to the DTC being determined not to be definitely unrelated to the collision event, determining, using the machine learning model, whether the DTC is related to the collision event by comparing a location of the component to a location of a point of impact associated with the collision event;
and removing the DTC from a repair estimate for the vehicle in response to the DTC being determined to be definitely unrelated to the collision event or not related to the collision event.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations above, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processor) to perform the process. In particular, the receiving step is recited at a high level of generality (i.e. as a general means of receiving information for use in the determining and other steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Furthermore, the addition of the “machine learning model” as an additional element is not sufficient to claim a practical application or amount to significantly more than a judicial exception as the limitation represents no more than mere instructions to apply the judicial exception on a computer and it can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers. Lastly, claims 1 and 16 further recite “A system, comprising: one or more hardware processors; and one or more non-transitory machine-readable storage media encoded with instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising” and “One or more non-transitory machine-readable storage media encoded with instructions that, when executed by one or more hardware processors of a computing system, cause the computing system to perform operations comprising” merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose vehicle control environment. See Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). The device(s) and processor(s) are recited at a high level of generality and merely automates the steps.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor and a machine learning model to perform the steps amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations discussed above are insignificant extra-solution activities.
The additional limitations of receiving information is a well-understood, routine and conventional activities and the specification does not provide any indication that the processor is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
Dependent claims 2-7, 9-14 and 16-20 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or additional elements that do not integrate the judicial exception into a practical application. The dependent claims are merely defining terms/concepts or have additional steps such as “comparing” and “determining”. Therefore, dependent claims 2-7, 9-14 and 16-20 are not patent eligible.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Brozovich (US 20200223385 A1) in view of Kwak (US 20210110480 A1) in further view of Guo CN111598850A (English Translation).
Regarding Claim 1, Brozovic teaches A system, comprising (see at least [¶07]):
one or more hardware processors (see at least [¶07 & 052]); and
one or more non-transitory machine-readable storage media encoded with instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising (see at least [¶07, 052 & 058]):
receiving a diagnostic trouble code (DTC), the DTC specifying diagnostic information related to a component in a vehicle damaged in a collision event, the diagnostic information comprising one or more data inputs generated by the component (Receiving a DTC, the DTC can have diagnostic information for vehicle components damaged after a collision. The diagnostic information can include data inputs generated by the components affected. see at least [¶031, 077 & 087-092]);
and removing the DTC from a repair estimate for the vehicle in response to the DTC being determined to be definitely unrelated to the collision event or not related to the collision event (Removing or adding a DTC from a repair estimate/report when the DTC is determined to either be unrelated to a collision event or related to a collision event. see at least [¶0208, 0221-0222 & 0231]).
Brozovic does not explicitly teach determining, using a machine learning model trained on similar historical repair data that includes categorized DTCs pertaining to collision events, whether the DTC is definitely unrelated to the collision event based on the one or more data inputs;
However, Kwak does teach determining, using a machine learning model trained on similar historical repair data that includes categorized DTCs pertaining to collision events, whether the DTC is definitely unrelated to the collision event based on the one or more data inputs (Determining, using a machine learning model with historical data of DTC and accident data, that a DTC could be related to an accident based on input data. It would be obvious that the DTC risk factor would lead to a DTC being the linked to a collision event. see at least [¶08, 020-021, 063 & 067]).
Kwak would be in a similar field as it also deals in the area of machine learning risk scoring system for vehicles. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Brozovic to use the technique of determining, using a machine learning model trained on similar historical repair data that includes categorized DTCs pertaining to collision events, whether the DTC is definitely unrelated to the collision event based on the one or more data inputs as taught by Kwak. Doing so would lead to an improved risk factor determination of a vehicle taking into account DTC and accident data using a machine learning model (see at least [¶08]).
Brozovic and Kwak do not explicitly teach in response to the DTC being determined not to be definitely unrelated to the collision event, determining, using the machine learning model, whether the DTC is related to the collision event by comparing a location of the component to a location of a point of impact associated with the collision event.
However, Guo does teach in response to the DTC being determined not to be definitely unrelated to the collision event, determining, using the machine learning model, whether the DTC is related to the collision event by comparing a location of the component to a location of a point of impact associated with the collision event (When a DTC is determined to be related to a collision/accident, determining that a DTC is connected to a collision/accident by comparing the location of the component with the area of damage in a vehicle using a machine learning model. see at least [¶033 & 035-039]).
Guo would be in a similar field as it also deals in the area of auditing vehicle damage using machine learning models. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Brozovic and Kwak to use the technique of having in response to the DTC being determined not to be definitely unrelated to the collision event, determining, using the machine learning model, whether the DTC is related to the collision event by comparing a location of the component to a location of a point of impact associated with the collision event as taught by Guo. Doing so would lead to improved efficiency of processing a vehicle auditing request (see at least [¶021]).
Regarding Claims 2, 9 and 16, Brozovic, Kwak and Guo teach all of the limitations of claims 1, 8 and 15 as shown above, furthermore, Brozovic teaches determining whether the location of the component is adjacent to the location of the point of impact (Determining that the location of the component is adjacent to a location/portion damaged by a collision. see at least [¶033 & 0207-0210]).
Regarding Claims 3, 10 and 17, Brozovic, Kwak and Guo teach all of the limitations of claims 1, 8 and 15 as shown above, furthermore, Brozovic teaches determining the DTC is unrelated to the collision event in response to determining that the location of the component is not adjacent to the location of the point of impact (Determining that the DTC is likely unrelated to the collision event when it is determined that the component is not adjacent to the point/location damaged by a collision. see at least [¶033 & 0207-0210]).
Regarding Claims 4, 11 and 18, Brozovic, Kwak and Guo teach all of the limitations of claims 1, 8 and 15 as shown above, furthermore, Brozovic teaches determining whether a type of the DTC is consistent with one or more DTCs previously determined to be associated with the collision event (Determining whether the type of DTC is similar/consistent to other DTC previously associated with collision events or just normal vehicle use. see at least [¶031-032, 0159-0162, 0212 & 0220]).
Regarding Claims 5, 12 and 19, Brozovic, Kwak and Guo teach all of the limitations of claims 1, 8 and 15 as shown above, furthermore, Brozovic teaches determining the DTC is unrelated to the collision event in response to determining that the type of the DTC is not consistent with the one or more DTCs previously determined to be associated with the collision event (Determining that a DTC is likely not related to a collision event when the type of DTC is not similar/consistent to other DTC previously associated with collision events. see at least [¶031-032, 0159-0162, 0212 & 0220]).
Regarding Claims 6, 13 and 20, Brozovic, Kwak and Guo teach all of the limitations of claims 1, 8 and 15 as shown above, furthermore, Brozovic teaches comparing the location of the component to the location of a point of impact associated with the collision event comprises: determining whether the location of the component is adjacent to the location of the point of impact (Determining that a DTC is related to a collision event based on comparing the location of a component affected by a DTC and the point of impact/damaged portion of the vehicle after a collision. This is achieved by determining that the location of the component is adjacent to a location/portion damaged by a collision. see at least [¶033 & 0207-0210]);
determining whether the DTC is related to the collision event further comprises: determining whether a type of the DTC is consistent with one or more DTCs previously determined to be associated with the collision event (Determining whether the type of DTC is similar/consistent to other DTC previously associated with collision events or just normal vehicle use. see at least [¶031-032, 0159-0162, 0212 & 0220])
and the operations further comprise: determining the DTC is related to the collision event in response to determining that (i) the location of the component is adjacent to the location of the point of impact and (ii) the type of the DTC is consistent with the one or more DTCs previously determined to be associated with the collision event (Determining that a DTC is likely related to a collision even by determining that the location of the component is adjacent to a location/portion damaged by a collision and whether the type of DTC is similar/consistent to other DTC previously associated with collision events or just normal vehicle use. While a DTC can be found to be collision from one determination type, the system can use multiple different determination types to reach a better conclusion. see at least [¶031-033 & 0208-0220]).
Regarding Claims 7 and 15, Brozovic, Kwak and Guo teach all of the limitations of claim 1 and 8 as shown above, furthermore, Brozovic teaches wherein the one or more data inputs comprises the location of the component (The data inputs can include the location of the component which is used to determine if a component is related to a collision. see at least [¶031, 077, 087-092 & 0208]).
Regarding Claim 8, Brozovic teaches One or more non-transitory machine-readable storage media encoded with instructions that, when executed by one or more hardware processors of a computing system, cause the computing system to perform operations comprising (see at least [¶06-08, 052 & 058]):
receiving a diagnostic trouble code (DTC), the DTC specifying diagnostic information related to a component in a vehicle damaged in a collision event, the diagnostic information comprising one or more data inputs generated by the component (Receiving a DTC, the DTC can have diagnostic information for vehicle components damaged after a collision. The diagnostic information can include data inputs generated by the components affected. see at least [¶031, 077 & 087-092]);
and removing the DTC from a repair estimate for the vehicle in response to the DTC being determined to be definitely unrelated to the collision event or not related to the collision event (Removing or adding a DTC from a repair estimate/report when the DTC is determined to either be unrelated to a collision event or related to a collision event. see at least [¶0208, 0221-0222 & 0231]).
Brozovic does not explicitly teach determining, using a machine learning model trained on similar historical repair data that includes categorized DTCs pertaining to collision events, whether the DTC is definitely unrelated to the collision event based on the one or more data inputs;
However, Kwak does teach determining, using a machine learning model trained on similar historical repair data that includes categorized DTCs pertaining to collision events, whether the DTC is definitely unrelated to the collision event based on the one or more data inputs (Determining, using a machine learning model with historical data of DTC and accident data, that a DTC could be related to an accident based on input data. It would be obvious that the DTC risk factor would lead to a DTC being the linked to a collision event. see at least [¶08, 020-021, 063 & 067]).
Kwak would be in a similar field as it also deals in the area of machine learning risk scoring system for vehicles. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Brozovic to use the technique of determining, using a machine learning model trained on similar historical repair data that includes categorized DTCs pertaining to collision events, whether the DTC is definitely unrelated to the collision event based on the one or more data inputs as taught by Kwak. Doing so would lead to an improved risk factor determination of a vehicle taking into account DTC and accident data using a machine learning model (see at least [¶08]).
Brozovic and Kwak do not explicitly teach in response to the DTC being determined not to be definitely unrelated to the collision event, determining, using the machine learning model, whether the DTC is related to the collision event by comparing a location of the component to a location of a point of impact associated with the collision event.
However, Guo does teach in response to the DTC being determined not to be definitely unrelated to the collision event, determining, using the machine learning model, whether the DTC is related to the collision event by comparing a location of the component to a location of a point of impact associated with the collision event (When a DTC is determined to be related to a collision/accident, determining that a DTC is connected to a collision/accident by comparing the location of the component with the area of damage in a vehicle using a machine learning model. see at least [¶033 & 035-039]).
Guo would be in a similar field as it also deals in the area of auditing vehicle damage using machine learning models. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Brozovic and Kwak to use the technique of having in response to the DTC being determined not to be definitely unrelated to the collision event, determining, using the machine learning model, whether the DTC is related to the collision event by comparing a location of the component to a location of a point of impact associated with the collision event as taught by Guo. Doing so would lead to improved efficiency of processing a vehicle auditing request (see at least [¶021]).
Regarding Claim 15, Brozovic teaches A computer-implemented method comprising (see at least [¶06-07]):
receiving a diagnostic trouble code (DTC), the DTC specifying diagnostic information related to a component in a vehicle damaged in a collision event, the diagnostic information comprising one or more data inputs generated by the component (Receiving a DTC, the DTC can have diagnostic information for vehicle components damaged after a collision. The diagnostic information can include data inputs generated by the components affected. see at least [¶031, 077 & 087-092]);
and removing the DTC from a repair estimate for the vehicle in response to the DTC being determined to be definitely unrelated to the collision event or not related to the collision event (Removing or adding a DTC from a repair estimate/report when the DTC is determined to either be unrelated to a collision event or related to a collision event. see at least [¶0208, 0221-0222 & 0231]).
Brozovic does not explicitly teach determining, using a machine learning model trained on similar historical repair data that includes categorized DTCs pertaining to collision events, whether the DTC is definitely unrelated to the collision event based on the one or more data inputs;
However, Kwak does teach determining, using a machine learning model trained on similar historical repair data that includes categorized DTCs pertaining to collision events, whether the DTC is definitely unrelated to the collision event based on the one or more data inputs (Determining, using a machine learning model with historical data of DTC and accident data, that a DTC could be related to an accident based on input data. It would be obvious that the DTC risk factor would lead to a DTC being the linked to a collision event. see at least [¶08, 020-021, 063 & 067]).
Kwak would be in a similar field as it also deals in the area of machine learning risk scoring system for vehicles. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Brozovic to use the technique of determining, using a machine learning model trained on similar historical repair data that includes categorized DTCs pertaining to collision events, whether the DTC is definitely unrelated to the collision event based on the one or more data inputs as taught by Kwak. Doing so would lead to an improved risk factor determination of a vehicle taking into account DTC and accident data using a machine learning model (see at least [¶08]).
Brozovic and Kwak do not explicitly teach in response to the DTC being determined not to be definitely unrelated to the collision event, determining, using the machine learning model, whether the DTC is related to the collision event by comparing a location of the component to a location of a point of impact associated with the collision event.
However, Guo does teach in response to the DTC being determined not to be definitely unrelated to the collision event, determining, using the machine learning model, whether the DTC is related to the collision event by comparing a location of the component to a location of a point of impact associated with the collision event (When a DTC is determined to be related to a collision/accident, determining that a DTC is connected to a collision/accident by comparing the location of the component with the area of damage in a vehicle using a machine learning model. see at least [¶033 & 035-039]).
Guo would be in a similar field as it also deals in the area of auditing vehicle damage using machine learning models. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Brozovic and Kwak to use the technique of having in response to the DTC being determined not to be definitely unrelated to the collision event, determining, using the machine learning model, whether the DTC is related to the collision event by comparing a location of the component to a location of a point of impact associated with the collision event as taught by Guo. Doing so would lead to improved efficiency of processing a vehicle auditing request (see at least [¶021]).
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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.
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/MOISES GASCA ALVA/Examiner, Art Unit 3667
/FARIS S ALMATRAHI/Supervisory Patent Examiner, Art Unit 3667