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 05/27/2025. Claims 1, 9 and 17 have been amended. Claims 1-20 are currently pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/27/2025 has been entered.
Response to Arguments
Applicant’s arguments submitted on 05/27/2025, with respect to the previous 35 U.S.C. 103 rejection of claims 1-20 have been fully considered and are persuasive. The 35 U.S.C. 103 rejection of claims 1-20 have been withdrawn.
Applicant’s arguments submitted on 05/27/2025, with respect to the previous 35 U.S.C. 101 rejection of claim 1 has been fully considered and is unpersuasive.
With respect to the previous 35 U.S.C. 101 rejection of claim 1, Applicant argues the amended claim recites specific technical operations for training machine learning models that cannot possible be performed in the human mind and therefore does not fall within the mental processes grouping. Applicant further contends that the claims recite a practical application and a technical improvement.
Examiner respectfully disagrees. While Applicant asserts that the claimed steps cannot be performed in the human mind due to computational complexity, the relevant inquiry is not whether the steps can be practically performed by a human at scale, but whether the claim recites concepts that are abstract in nature, such as data analysis, mathematical relationships, or mental processes. The claimed steps of “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” constitute data evaluation, comparison and mathematical weighting operations, which fall within the mathematical concepts grouping and are also mental processes. These steps may require a computer to perform efficiently but does not remove them from an abstract idea. The Federal Circuit has repeatedly held that claims directed to selection, weighting and model training remain abstract even when large datasets or significant computational resources are involved – See e.g. Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016) (claims directed to collecting, analyzing and displaying data held abstract). Applicant further asserts that the claim integrates the abstract idea into a practical application by reciting machine-learning based operation. However, the claim does not recite any specific technical mechanism or improvement to computer functionality itself. In particular, the claim fails to improve the operation of a computer, processor or memory system. Rather, the claim merely applies the abstract idea of selecting and weighting data using generic computing components in the context of vehicles. Limiting the abstract idea to a particular technological field, without more, does not amount to a practical application. Furthermore, considering the claim elements individually and as an ordered combination, the additional elements merely recite well-understood, routing and conventional activities associated with machine learning, including data selection, data weighting, and model training. Accordingly, the claim remains directed to an abstract idea under Step 2A, Prong One and fails to integrate that abstract idea into a practical application under Step 2A, Prong Two. Further, the claim does not include an inventive concept sufficient to confer eligibility under Step 2B.
The rejection under 35 U.S.C. 101 is therefore maintained.
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, receiving event data associated with a vehicle; 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 mathematical concepts and 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 receiving event data associated with a vehicle; 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, 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:
The claim recite additional elements to the abstract idea. However, these additional elements fails to integrate into a practical application.
Claim 1 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. Which is mere data gathering that is simply employed as a tool to collect information and reporting results, which is insignificant extra solution activity as the step simply gathers data necessary to perform the abstract idea. These additional steps amount necessary data gathering and reporting results, wherein all uses of the recited abstract idea require such data gathering or data output. See MPEP 2106.05(g).
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. And as discussed above, the additional limitations of 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 examiner submits that these limitations are insignificant extra- solution activities.
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, 8 and 15 are ineligible under 35 U.S.C. 101.
Regarding claims 2 and 10 recite generating one or more features based on the event data, wherein generating one or more features based on the event data includes generating a set of aggregated events based on the event data. This limitation further narrows the abstract idea, but are nonetheless part of the abstract idea. Therefore, these claims are similarly rejected under the same rationale as claims 1 and 11.
Regarding claims 3, 11 and 18 recite wherein the set of aggregated events includes events selected from the group consisting of acceleration rates, braking rates, maximum speeds, road conditions, mileage, and component statuses. This limitation further narrows the abstract idea, but are nonetheless part of the abstract idea. Therefore, these claims are similarly rejected under the same rationale as claims 1 and 11.
Regarding claims 4, 12 and 19 recite wherein the training data set comprises a target event from the event data and a time series of events in the event data occurring prior to the target event. This limitation further narrows the abstract idea, but are nonetheless part of the abstract idea. Therefore, these claims are similarly rejected under the same rationale as claims 1 and 11.
Regarding claim 5 recites determining the time series of events by utilizing a fixed window for identifying event data prior to the target event. This limitation further narrows the abstract idea, but are nonetheless part of the abstract idea. Therefore, these claims are similarly rejected under the same rationale as claims 1 and 11.
Regarding claims 6, 7, 14-16 and 20 recite wherein the second model is associated with a type of the vehicle. This limitation further narrows the abstract idea, but are nonetheless part of the abstract idea. Therefore, these claims are similarly rejected under the same rationale as claims 1 and 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sellinger et al., US 20180307979 A1 discloses training a neural network on a first host system, sending the neural network to a plurality of second host systems, training the neural network by each second host system on data private to each second host system, and sending the updated neural network coefficients to the first host system to create a composite neural network based on data private to the plurality of second host systems.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin Bishop can be reached at 5712703713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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RESPECTFULLY SUBMITTED
/MAHMOUD M KAZIMI/Examiner, Art Unit 3665