DETAILED ACTION
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 Office Action is a Non-Final Office Action. Claims 1-20 are currently pending and addressed below; claims 1-20 have been amended.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/2/2026 has been entered.
Response to Amendment
In response to Applicant’s amendments, Examiner withdraws the previous claim objections; withdraws the previous § 112(b) rejections; withdraws the previous § 103 rejections; adds the below claim objections; and maintains the previous § 101 rejections.
Response to Arguments
Applicant’s arguments, see Remarks, filed 7/2/2026, with respect to the § 103 rejections have been fully considered and are persuasive. The § 103 rejection of claims 1-20 has been withdrawn.
Applicant's arguments filed 7/2/2026 with respect to the § 101 rejection have been fully considered but they are not persuasive.1
Rejections under 35 USC § 101
First, Applicant argues that the § 101 rejection of claims 1-20 should be withdrawn because “[t]he claims…integrate any mathematical concepts into a practical application” because the claims “recite a concrete process for reliably detecting and classifying physical changes to roadway infrastructure from real-world sensor data and then enabling the updating of map data used by vehicles.” Remarks at p. 11. Examiner respectfully disagrees. Receiving real-world sensor data and updating map data is simply an abstract idea with an insignificant extra-solution activity of data gathering the is required to perform the abstract idea (See § 101 rejection below for more details). Furthermore, the claims, as presently written, recite no more than receiving data and analyzing, calculating and/or comparing received data to make a determination. In other words, a computer receives data and performs calculations and nothing comes out of the computer. The generation of a notification isn’t even an extra-solution activity because it does not get sent to any external database or used for any vehicle navigation. Therefore, the claims do not integrate the mathematical concepts into a practical application and Applicant’s arguments are unpersuasive.
Second, Applicant argues that the claims improve “the technology of dynamic map maintenance based on real-world vehicle sensor data to ensure the accuracy and reliability of roadway map data used for vehicle navigation and autonomous control.” Remarks at pp. 11-12. Examiner respectfully disagrees. As set forth in the previous Office Action, Applicant is reminded that “the ‘improvements’ analysis in Step 2A determines whether the claim pertains to an improvement to the functioning of a computer or to another technology without reference to what is well-understood, routine, conventional activity. That is, the claimed invention may integrate the judicial exception into a practical application by demonstrating that it improves the relevant existing technology although it may not be an improvement over well-understood, routine, conventional activity. It should be noted that while this consideration is often referred to in an abbreviated manner as the ‘improvements consideration,’ the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field.” MPEP § 2106.04(d)(1).
The technology as issue in claim 1 is not simply updating maps. The technology is computer-based vehicle map updating, which requires an improvement to the computing system capabilities and/or functionality. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336 (Fed. Cir. 2016). Moreover, ¶ [0018] of the present specification describes the invention as increasing the timeliness, or speed, of identifying roadway features, and the Federal Circuit has held that an increase of speed in processing data using general-purpose computers, as is the case here, is not sufficient to show an improvement in computer functionality. MPEP § 2106.05(a) (citing FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, (Fed. Cir. 2016)). Applicant has not alleged an improvement to the computer system used to update the vehicle map, nor does the specification provide an improvement sufficient to improve the computer functionality.
Assuming, arguendo, that an improvement to the computing system itself is not required, the claims do not improve the technology of dynamic map maintenance based on real-world vehicle sensor data to ensure the accuracy and reliability of roadway map data used for vehicle navigation and autonomous control because the claims do not provide access to the updated lane change information for use in vehicle navigation. In fact, the updated data is not being used for any vehicle navigation, now or in the future because the update is not sent to an external database where it is used for actual vehicle navigation. If a vehicle were being driven or controlled autonomously via the updated map data then the claims would not longer recite an abstract idea. Applicant is also reminded that “the judicial exception alone cannot provide the improvement.” MPEP § 2106.05(a). Therefore, the claims do not recite an improvement to the technology and Applicant’s arguments are unpersuasive.
Third, Applicant further argues that “a comparison of the claims to Example 47, Claim 2 [of the July 2024 AI Subject Matter Eligibility Guidance] is not apt.” Remarks at p. 12. Examiner respectfully disagrees.
Applicant argues that the data collected isn’t insignificant extra-solution activity, but rather a necessary step to enable detect of changes in lane markings. Remarks at p. 12. Whether the step of data gathering is a necessary step does not change the fact that the court have recognized such data gathering as well-understood, routine, and conventional functions. buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014). Therefore, Applicant’s argument is unpersuasive.
Applicant further argues that the claims require specific rules for identifying a change based on fitness metrics and the type of change, and that this is not simply apply the GMM, but rather recites specific details of how a solution to a problem is accomplished. Remarks at p. 12. Regardless of the complexity of the math being performed, the claims still recites mathematics being performed in a black box computer. As mentioned above, data is input into a computer, math is performed, and that is the end of the invention. The complexity of the math weighs on the patentability of the invention, but not whether it is an abstract idea. Therefore, Applicant’s argument is unpersuasive.
Applicant further argues that “the notification is specifically generated based on change and change type to indicate how a specific lane marking needs to be changed with respect to a map database, thereby enabling updated navigation data to future vehicles,” and that “[t]his is not merely post-solution activity – it is an integral part of the process for improving accuracy and reliability of map data used in vehicle navigation and autonomous control described in the specification.” Remarks at pp. 12-13. Examiner first notes that the notification limitation does not even amount to post-solution activity because it is not transmitted to an external source to actually update a database. Rather, it remains within the computer system itself. In other words, the notification is generated to update a map database, but the claims do not positively recite the step up actually updating the map database. Furthermore, while the specification may describe vehicle navigation and autonomous control, the claims do not. Therefore, Applicant’s argument is unpersuasive.
Examiner would like to further note the similarities between the present claim 1 and claim 2 of Example 47, as charted below:
Claim 1 of Present App
Claim 2 of Example 47
receive a first and a second data set of lane marking positions collected as lateral offsets from a center of a roadway via sensors of vehicles traversing the roadway
(a) receiving, at a computer, continuous training data;
(b) discretizing, by the computer, the continuous training data to generate input data;
train a Gaussian mixture model (GMM) based on a first data set to represent lane marking positions and to obtain a first fitness metric
(c) training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm;
updated the GMM based on the second data set to obtain a second fitness metric; determine that a lane marking change on the roadway has occurred when a difference between the first fitness metric and the second fitness metric is greater than a threshold amount;
(d) detecting one or more anomalies in a data set using the trained ANN;
identify a change type based on a change in width or number of Gaussian curves within the GMM
(e) analyzing the one or more detected anomalies using the trained ANN to generate anomaly data; and
generate a notification of the lane marking change and the change type to update a map database that provides updated navigation data to future vehicles traversing the roadway
(f) outputting the anomaly data from the trained ANN.
Therefore, Applicant’s arguments are unpersuasive and Examiner maintains the § 101 rejection of claims 1-20.
Claim Objections
Claims 1, 8, and 14 are objected to because of the following informalities:
Line 8 of claim 1 should recite –based on the first data set—rather than “based on a first data set” to clarify that the limitation is referencing the previously introduced first data set;
Line 5 of claim 8 should recite –based on the first data set—rather than “based on a first data set” to clarify that the limitation is referencing the previously introduced first data set;
Line 4 of claim 14 should recite –based on the first data set—rather than “based on a first data set” to clarify that the limitation is referencing the previously introduced first data set; and
Line 2 of claim 14 contains a period before the limitation beginning with “receiving…” This appears to be a typographical error.
Appropriate correction is required.
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 they recite an abstract idea without significantly more.
101 Analysis - Step 1
Claims 1-8 recite a system, therefore claims 1-8 are a machine, which is within at least one of the four statutory categories.
Claims 8-13 recite a non-transitory machine-readable medium, therefore claims 8-13 are a machine, which is within at least one of the four statutory categories.
Claims 14-20 recite a method, therefore claims 14-20 are a process, which is within at least one of the four statutory categories.
101 Analysis - Step 2A, Prong 1
Regarding Prong 1 of the Step 2A analysis, 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. Claim 1 recites:
A system, comprising:
a processor; and
a memory storing machine-readable instructions that, when executed by the processor, cause the processor to:
receive a first and a second data set of lane marking positions collected as lateral offsets from a center of a roadway via sensors of vehicles traversing the roadway;
train a Gaussian mixture model (GMM) based on a first data set to represent lane marking positions and to obtain a first fitness metric;
updated the GMM based on the second data set to obtain a second fitness metric;
determine that a lane marking change on the roadway has occurred when a difference between the first fitness metric and the second fitness metric is greater than a threshold amount;
identify a change type based on a change in width or number of Gaussian curves within the GMM; and
generate a notification of the lane marking change and the change type to update a map database that provides updated navigation data to future vehicles traversing the roadway.
These limitations, as drafted, is a method that, under its broadest reasonable interpretation, covers performance of the limitation as certain methods of organizing human activity/in the human mind. That is, nothing in the claim elements preclude the steps from practically being performed as human activity/in the mind. For example, “train…,” “update…,” “determine,” “identify…,” and “generate...,” encompass a human receiving data and comparing it to historical data to determine if there is a difference in the data and/or receiving data and performing mathematical calculations. Thus, the claims recite at least one abstract idea.
101 Analysis - Step 2A, Prong 2
Regarding Prong 2 of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. 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:
a processor; and
a memory storing machine-readable instructions that, when executed by the processor, cause the processor to:
receive a first and a second data set of lane marking positions collected as lateral offsets from a center of a roadway via sensors of vehicles traversing the roadway;
train a Gaussian mixture model (GMM) based on a first data set to represent lane marking positions and to obtain a first fitness metric;
updated the GMM based on the second data set to obtain a second fitness metric;
determine that a lane marking change on the roadway has occurred when a difference between the first fitness metric and the second fitness metric is greater than a threshold amount;
identify a change type based on a change in width or number of Gaussian curves within the GMM; and
generate a notification of the lane marking change and the change type to update a map database that provides updated navigation data to future vehicles traversing the roadway.
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.
The limitation of “receive a first data set and a second data set” merely describes how to generally gather data, i.e., receive, recited at a high level of generality, and thus in an insignificant extra-solution activity. See MPEP § 2106.05(g) (“whether the limitation is significant”). In addition, the uses of the recited judicial exception require such data gathering and, as such, this limitation does not impose any meaningful limits on the claim. The limitation amounts to necessary data gathering.
Taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations 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 or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular process for receiving data and comparing it to historical data to determine if there is a difference in the data, 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).
Furthermore, the additional elements of a processor and memory are mere instructions to apply the above-noted abstract idea by using a general processor and computer system to perform the process. In particular, the devices recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, the additional limitations do 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, representative independent claim 1 does not include additional elements (considered both individually and as an ordered 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 element of memory and processors receiving data and comparing it to historical data to determine if there is a difference in the data amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions cannot provide an inventive concept. Moreover, the “receiv[ing] a first data set and a second data set…” amounts to nothing more than insignificant extra solution activities.
A conclusion that an additional element is insignificant extra solution activity in Step 2A must be re-evaluated in Step 2B to determine if the element is more than what is well-understood, routine, and conventional in the field. In this case, the additional limitation of “receiv[ing] a first data set and a second data set…” is well-understood, routine, and conventional activities. Additionally, the remaining elements have all been deemed insignificant extra solution activity by one or more Courts; see at least MPEP 2106.05(d) and MPEP 2106.05(g):
a. data gathering… is considered well-understood, routine, and conventional activity under 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 F.3d 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).
Because the claims fail to recite anything sufficient to amount to significantly more than the judicial exception, independent claims 1, 8, and 14 are patent ineligible under 35 U.S.C. 101.
Dependent claims 2-7, 9-13, and 15-20 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 well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Specifically, claims 3, 5, 7, 10, 12, 16, 18, and 20 are directed toward additional aspects of the judicial exception (“the GMM probabilistically models the lane marking positions…,” “the first fitness metric and the second fitness metric are determined using at least one of a Bayesian Information Criterion…,” “training the model in at least one of a supervised mode using ground-truth…,”; and claims 2, 4, 6, 11, 13, 15, 17, and 19 are directed to the insignificant extra-solution activity of data gathering (“the sensors comprise…,” “the first data set and the second data set further comprise metadata…,” “receive at least one of the first data set or the second data set via at least one of vehicle-to-vehicle communication”). Therefore, dependent claims 2-7, 9-13, and 15-20 are not patent eligible under the same rationale as provided for in the rejection of claims 1, 8, and 14.
Examiner encourages Applicant to request an interview to discuss proposed claim language for overcoming the current rejections under § 101.
Potential Allowable Subject Matter
Claims 1-20 could be allowable if Applicant overcomes the above § 101 rejections.
The following is a statement of reasons for the indication of potential allowable subject matter:
The combination of claim limitations of determine that a lane marking change on the roadway has occurred when a difference between the first fitness metric and the second fitness metric is greater than a threshold amount, identify a change type based on a change in width or number of Gaussian curves within the GMM, and generate a notification of the lane marking change and the change type to update a map database of claims 1, 8, and 14, when considered with all other claim features contained in the claims from which claims 1, 8, and 14 depends, renders the claim, as well as its dependents, novel and non-obvious over the prior art of record.
The closest prior art, Beaurepaire and Zang, teaches receiving a first and second data set of lane markings, training a machine learning model on the first data set of lane markings, inputting the second data set into the machine learning model, determining a difference in the output, identifying a lane change, and generating a notification of the change. However, as pointed out by Applicant on pages 13-14 of the Response dated 7/2/2026, the combination of Beaurepaire and Zang fails to teach training a GMM on a first data set to obtain a first fitness metric, then updating the GMM based on a second data set to obtain a second fitness metric, determining that lane marking change has occurred based on a comparison of the two fitness metrics exceeding a threshold, and identifying the type of the change based on a change in the width or number of Gaussian curves within the GMM, followed by updating a map database with both the change and the identified change type. Applicant’s argument is persuasive.
As such, the combination of Beaurepaire and Zang does not teach the combination of determine that a lane marking change on the roadway has occurred when a difference between the first fitness metric and the second fitness metric is greater than a threshold amount, identify a change type based on a change in width or number of Gaussian curves within the GMM, and generate a notification of the lane marking change and the change type to update a map database, as required by claims 1, 8, and 14. No other prior art has been found which remedies the deficiencies of the Beaurepaire and Zang combination. Therefore, the claims 1, 8, and 14 would be allowable over the prior art. Claims 2-7, 9-13, and 15-20 depend from claims 1, 8, and 14 and would be allowable for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Pat. No. 12,359,934 to Guerrero et al. teaches use of Gaussian Bayes algorithms in machine learning models to validate lane markings (Figures 3 and 4 and description thereof; Col. 21, l. 25 – Col. 23, l. 10);
U.S. Pub. No. 2023/0298363 to Zhang et al. teaches collecting sensor data, identifying lane markings in the data, determining changes to the lanes, and updating a lane marking database (Figure 9 and description thereof);
U.S. Pub. No. 2023/0192103 to Lin et al. teaches use of sensor data to detect lane markings to determine valid markings and to prevent misdetected lane markings (¶¶ [0057] – [0067]);
U.S. Pub. No. 2021/0362723 to Cunha et al. teaches use of Gaussian curves to detect lane markings (¶ [0036]).
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/JOHN D HOLMAN/Examiner, Art Unit 3667
1 Examiner left Applicant’s attorney of record a voicemail on 7/21/2026 to discuss potential amendments to overcome the § 101 rejection, but has not received a call back. Examiner encourages Applicant’s to schedule an interview to discuss the § 101 rejection.
Prosecution Insights