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 .
Status of Claims
This communication is in response to Application# 18/397,237 filed on 04/17/2026. Claims 1-20 are currently pending.
Response to Arguments
Applicant’s arguments submitted on 04/17/2026, with respect to the previous 35 U.S.C. 101 rejection of claim 1 has been fully considered and rendered moot in view of the revised and refined eligibility analysis set forth herein.
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.
Under Step 2A – Prong 1:
Claims 1, 9 and 17 recites the abstract idea concept of a method (Claim 1), a non-transitory computer-readable storage medium (claim 9) and a device (claim 17) of monitoring and analyzing vehicle data. This abstract idea is described in at least claims 1, 9 and 17 by, generating a training data set based on the event data, and selecting a second training data from a plurality of vehicles based on type of the vehicle and weighting the selected training data based on regional factors associated with the plurality of vehicles are considered mental process steps. The identified claim limitations that recite an abstract idea fall within the enumerated groupings of abstract ideas in Section 1 of the 2019 Revised Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019.
The limitations of generating a training data set based on the event data, and selecting a second training data from a plurality of vehicles based on type of the vehicle and weighting the selected training data based on regional factors associated with the plurality of vehicles as drafted, are process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting generating a training data set based on the event data, selecting a second training data and weighting the selected training data nothing in the claim elements precludes the step from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, claim 1 recites an abstract idea.
Under Step 2A – Prong 2:
Claim 1 recites additional elements beyond the identified abstract idea. However, the additional elements, individually or in combination, do not integrate the judicial exception into a practical application.
Claim 1 further recites, receiving event data associated with a vehicle, which constitutes mere data gathering because it obtains the information used in the subsequent generation, selection, weighting and model training operations. This limitation is insignificant pre-solution activity and does not impose a meaningful limit on the identified abstract idea. Claim 1 additionally recites training a first model using the training data set, the first model associated with the vehicle and training a second model using the training data set and a second training data set associated with at least one other vehicle. The model training limitations are not characterized as insignificant extra solution activity, rather they are additional elements recited at a high level of generality that invoke broadly defined model training functionality without specifying a particular model architecture, training algorithm, parameter adjustment procedure, or technical interaction between first and second models. Considered individually and as an ordered combination with the identified abstract idea, the additional elements do not improve the functioning of a computer, the operation of a machine learning model, or another technology or technical field. The claim does not recite how either model is technically improved or how the models interact. Instead, the claim generally instructs that models be trained using information generated, selected and weighted according to the recited informational criteria.
Under Step 2B:
Regarding Step 2B of the 2019 PEG, independent claim 1 does not include additional elements (considered both individually or in combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount 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.
Further, a conclusion that an additional element is insignificant extra solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood routine and convention activity in the field. The additional limitations are well-understood, routine and conventional activities. Examiner relies on what the courts have recognized, or those or ordinary skill in the art would recognize, as elements that describe well-understood, routine, and conventional activity in particular fields. For example, receiving or transmitting data over a network, e.g., using the internet to gather data, Symantec 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., V Amazon.com, Inc., 788 F3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed Cir. 2015) (Sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result--a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)). In this case, the use of devices and networks is described at a high level of generality, or as an insignificant extra-solution activity that cannot be considered as an improvement to network/computer technology. Further the mere collection of data or receipt of data over a network is a well-understood, routine and conventional function when it is claimed in a merely generic manner (as itis here). See MPEP 2106.05(d). Therefore, claims 1, 9 and 17 are ineligible under 35 U.S.C. 101.
Dependent claim(s) 2-8, 10-16 and 18-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-8, 10-16 and 18-20 are not patent eligible under the same rationale as provided for in the rejection of claims 1, 9 and 17.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Barfield et al., US 20160035150 A1 discloses analysis of vehicle data to predict potential component failures or other mechanical failures relating to the vehicle. The vehicle data that is analyzed may include diagnostic information from an OBD port of the vehicle, information available from the vehicle CAN BUS (controller area network bus), data sensed by other sensors associated with the vehicle (e.g., in an onboard telematics device), data relating to drivers of the vehicle, data relating to the particular type of vehicle, data relating to environmental conditions through which the vehicle is driven, and/or other data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAHMOUD M KAZIMI whose telephone number is (571)272-3436. The examiner can normally be reached M-F 7am-5pm.
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RESPECTFULLY SUBMITTED
/MAHMOUD M KAZIMI/Examiner, Art Unit 3665
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