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 made FINAL. Claims 1-20 are currently pending and addressed below; claims 1-4, 6-11, 13-17, 19, and 20 have been amended.
Response to Amendment
In response to Applicant’s amendments, Examiner maintains the previous § 101 rejections; maintains the previous § 103 rejections; and adds the below § 112(b) rejections.
Response to Arguments
Applicant's arguments filed 2/23/2026 have been fully considered but they are not persuasive.
Rejections under 35 USC § 101
First, Applicant argues that the § 101 rejection of claims 1-20 should be withdrawn because “the claims as amended provide an ordered combination improving the technical process of updating vehicle maps and is therefore not directed to an abstract idea under Step 2A Prong 1.” Remarks at p. 10 (citing McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1316 (Fed. Cir. 2016)). Examiner respectfully disagrees. 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. As such, Applicant’s argument is unpersuasive.
Second, Applicant further argues that “unlike Example 47, Claim 2 [of the July 2024 AI Subject Matter Eligibility Guidance], where the claims merely require outputting the anomaly data from a neural network, the amended independent claims require evaluating the comparison of the GMM outputs in terms of different fitness metrics to determine if a lane marking change has occurred and generating a notification based on that evaluation.” Remarks at p. 10. Applicant further argues that the present claims differ from the July 2024 Guidance because “the ordered combination recites particular rules that achieve a concrete technological improvement in crowdsourced lane-marking change detection and real-time map freshness.”1 Id.
Examiner notes that there is no description in the specification of crowdsourced lane-marking change detection. Therefore, any arguments directed to crowdsourced lane-marking change detection is unpersuasive since that feature is not claimed, nor is it described in the specification. As such, Applicant’s argument is unpersuasive for that reason alone.
Addressing Example 47, Claim 2 of the July 2024 Guidance, Applicant seems to be arguing that because the example uses continuous data to training the neural network, and the claimed invention has limits on the data used to train the neural network, that there is a meaningful difference between Example 47, Claim 2 and the claim invention of the present application. Remarks at p. 10 (“…the amended claims put limits on the data used to train the GMM…”). Examiner finds no distinction between use of continuous data versus a collected data set, nor is the first data set of claim 1 limit to non-continuous data used to train the neural network.2 Furthermore, the claimed invention of the present application is akin to the example in that there is a neural network trained on a data set, inputting a second data set into the trained neural network, and comparing the output from the two data sets to detect an anomaly. The anomaly detected in the claim invention is whether there is a change in lane marking between the two data sets. There is no distinction between anomalies associated with lane markings and the anomalies being detect in Example 47, Claim 2.
Therefore, Applicant’s arguments are unpersuasive and Examiner maintains the § 101 rejection of claims 1-20.
Rejections under 35 USC § 103
Applicant generically argues that Beaurepaire and Zang fail to teach or suggest claim 1. Remarks at p. 11. Specifically, Applicant states that the cited references fail to “teach or suggest any of the following ordered, specific limited [sic] in amended claim 1:,” and then proceeds to list every single limitation of claim 1. Id. Applicant then makes the same statement with each of the limitations summarize, while still failing to provide any detail as to what specifically is allegedly not taught by the prior art. This is arguably an improper response for not pointing out specifically what is alleged to be deficient from the cited prior art that would allow Examiner to respond. As such, Examiner directs Applicant to claim 1 mapped below where each of the limitations being argued can be found in the prior art. Therefore, Applicant’s arguments are unpersuasive.
Applicant’s only substantive remark with respect to the prior art is that “the references in combination only address temporary confusion indexing as opposed to lane marker change detection arising from evaluating GMM statistical fit.” Remarks at p. 11. Examiner respectfully disagrees with Applicant’s oversimplification of the teachings of Beaurepaire and Zang. As set forth in detail below, Beaurepaire teaches lane marker change detection arising from evaluating a statistical fit using a trained neural network (¶ [0058] describing training the machine learning model on lane marking positions for a first fitness index, and describing identifying a change in the lane markings between the received data and the historical data); ¶¶ [0030], [0031], [0039] describing collecting data from a plurality of vehicles traveling a roadway that represent lane markings to input into a machine learning model); ¶ [0057] describing updating or improving the machine learning model based on the data collected; ¶ [0056] describing comparing the received data to the historical data in the machine learning model). Zang teaches use of a GMM to detect lane mark changes (¶ [0070], [0072] describing use of Gaussian model and Gaussian curves to determine lane markings). The combination of Beaurepaire and Zang teaches lane marker detection arising from evaluating GMM statistical fit. Therefore, Applicant’s arguments are unpersuasive.
In conclusion, Applicant’s arguments with respect to claims 1, 9, and 15 are unpersuasive for all the reasons set forth above. Applicant has not provided any independent arguments for dependent claims 2-8, 10-14, and 16-20.
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 9-14 recite a non-transitory machine-readable medium, therefore claims 9-14 are a machine, which is within at least one of the four statutory categories.
Claims 15-20 recite a method, therefore claims 15-20 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:
train a Gaussian mixture model (GMM) to represent lane marking position data based on a first data set of lane marking positions collected from vehicle traversing a roadway and obtain a first fitness metric;
provide a second data set of lane marking positions collected from the vehicles traversing the roadway as input to the GMM;
updated the GMM based on the second data set to obtain a second fitness metric;
determine where a change in the lane marking on the roadway has occurred based on evaluating a comparison of the first fitness metric and the second fitness metric; and
generate a notification of the change in the lane marking.
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…,” “provide...,” “update…,” “determine,” and “generate...,” encompass a human receiving data and comparing it to historical data to determine if there is a difference in the data. 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:
train a Gaussian mixture model (GMM) to represent lane marking position data based on a first data set of lane marking positions collected from vehicle traversing a roadway and obtain a first fitness metric;
provide a second data set of lane marking positions collected from the vehicles traversing the roadway as input to the GMM;
updated the GMM based on the second data set to obtain a second fitness metric;
determine where a change in the lane marking on the roadway has occurred based on evaluating a comparison of the first fitness metric and the second fitness metric; and
generate a notification of the change in the lane marking.
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.
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).
These additional limitations 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, 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 a processor and memory 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. Hence, the claim is not patent eligible.
Therefore, claim 1 is ineligible under 35 USC §101. Independent claims 9 and 15 are ineligible for the same reasons.
Dependent claims 2-8, 10-14, and 16-20 specifies limitations that elaborate on the abstract idea of claims 1, 9, and 15, and thus are directed to an abstract idea, do not recite additional limitations that integrate the claim into a practical application or amount to “significantly more” for similar reasons.
Examiner encourages Applicant to request an interview to discuss proposed claim language for overcoming the current rejections under § 101.
Claim Objections
Applicant is advised that should claim 2 be found allowable, claim 3 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 9-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 recites the limitation “the first fitness metric” in line 14. There is insufficient antecedent basis for this limitation in the claim.
Claim 10 recites the limitation “the first fitness metric” in line 10. There is insufficient antecedent basis for this limitation in the claim.
Claims 11-14 depend from claim 9 and are indefinite for the same reason.
Claim 15 recites the limitation “the first fitness metric” in line 13. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation “the first fitness metric” in line 7. There is insufficient antecedent basis for this limitation in the claim.
Claims 17-20 depend from claim 15 and are indefinite for the same reason.
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 (i.e., changing from AIA to pre-AIA ) 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 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 U.S. Pub. No. 2023/0358558 to Beaurepaire et al. (previously of record) in view of U.S. Pub. No. 2019/0130182 to Zang et al. (previously of record).
Regarding claim 1, Beaurepaire et al. discloses:
A system, comprising:
a processor; and
a memory storing machine-readable instructions that, when executed by the processor (¶ [0109] memory and processor), cause the processor to:
train a [machine learning model] to represent lane marking position data based on a first data set of lane marking positions collected from vehicles travering a roadway and obtain a first fitness metric (¶ [0058] describing training the machine learning model on lane marking positions for a first fitness index);
provide a second data set of lane marking positions collected from the vehicles traversing the roadway as input to the [machine learning model] (¶¶ [0030, [0031], [0039] describing collecting data from a plurality of vehicles traveling a roadway that represent lane markings to input into a machine learning model);
update the [machine learning model] based on the second data set to obtain a second fitness metric (¶ [0057] describing updating or improving the machine learning model based on the data collected);
determine a fit of the model to a combination of the historic lane marking position data and the data set (¶ [0056] describing comparing the received data to the historical data in the machine learning model);
determine where a change in a lane marking on the roadway has occurred based on evaluating a comparison of the first fitness metric and the second fitness metric (¶ [0058] describing identifying a change in the lane markings between the received data and the historical data); and
generate a notification of the change in the lane marking (¶¶ [0034], [0072] describing generating a notification of the change in the land marking).
Beaurepaire et al. does not expressly disclose that the learning model is a Gaussian Mixture model.
Zang et al., in the same field of endeavor, teaches use of a Gaussian Mixture model for identifying lane markings (¶ [0070], [0072] describing use of Gaussian model and Gaussian curves to determine lane markings).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Beaurepaire et al.’s invention to incorporate use of a Gaussian Mixture model as the machine learning model, as taught by Zang et al., with a reasonable expectation of success in determining changes in lane marking because a person of ordinary skill in the art would recognize the modification to be a mere simple substitution of one known learning model for another known learning model resulting in predictable results (Zang et al. at ¶ [0072]).
Regarding claim 2, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 1. Beaurepaire et al. further discloses:
wherein:
evaluating the comparison includes to identify the change based on a difference between the first fitness metric and the second fitness metric being greater than a threshold amount (¶ [0056] describing comparing the received data to the historical data in the machine learning model; ¶ [0036] describing the minimum and maximum threshold levels used to determine the probability of a change in the lane markings).
Regarding claim 3, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 1. Beaurepaire et al. further discloses:
wherein:
evaluating the comparison includes to identify the change based on a difference between the first fitness metric and the second fitness metric being greater than a threshold amount (¶ [0056] describing comparing the received data to the historical data in the machine learning model; ¶ [0036] describing the minimum and maximum threshold levels used to determine the probability of a change in the lane markings).
Regarding claim 4, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 1. Beaurepaire et al., as modified by Zang et al. further discloses:
wherein the machine-readable instructions further comprise a machine-readable instruction that, when executed by the processor, causes the processor to identify a type of the change in the lane marking on the roadway based on a shape change of Gaussian curves that define the GMM responsive to provision of the second data set as input to the GMM (Zang et al. at ¶ [0072] describing determining whether the lane markings match the Gaussian curves).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Beaurepaire et al.’s invention to incorporate use of a Gaussian Mixture model and the shape of Gaussian curves, as taught by Zang et al., with a reasonable expectation of success in determining changes in lane marking because a person of ordinary skill in the art would recognize the modification to be a mere simple substitution of one known learning model for another known learning model resulting in predictable results (Zang et al. at ¶ [0072]).
Regarding claim 5, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claims 4. Zang et al. further discloses:
wherein the machine-readable instruction that, when executed by the processor, causes the processor to identify the type of the change in the lane marking on the roadway comprises at least one of:
a machine-readable instruction that, when executed by the processor, causes the processor to identify a change in a number of lanes on the roadway based on the shape change of the Gaussian curves; or
a machine-readable instruction that, when executed by the processor, causes the processor to identify a shift in lanes on the roadway based on the shape change of the Gaussian curves (Zang et al. at ¶ [0072] describing determining whether the lane markings match the Gaussian curves, which represents a shift in the lanes on the roadway).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Beaurepaire et al.’s invention to incorporate use of a Gaussian Mixture model and Gaussian curves, as taught by Zang et al., with a reasonable expectation of success in determining changes in lane marking because a person of ordinary skill in the art would recognize the modification to be a mere simple substitution of one known learning model for another known learning model resulting in predictable results (Zang et al. at ¶ [0072]).
Regarding claim 6, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 1. Beaurepaire et al. further discloses:
wherein the machine-readable instructions further comprise a machine-readable instruction that, when executed by the processor, causes the processor to analyze metadata associated with the first data set and the second data set to determine a change to a type of the lane marking (¶ [0109] describing analyzing metadata associated with both data sets used to determine lane marking changes).
Regarding claim 7, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 1. Beaurepaire et al. further discloses:
wherein the machine-readable instruction that, when executed by the processor, causes the processor to provide the data set of lane marking positions comprises a machine-readable instruction that, when executed by the processor, causes the processor to provide a data set indicating multiple days of collected lane marking position data (¶ [0079] describing collecting lane marking positions over the course of hours, days, weeks, etc. and the historical lane marking data of the machine learning model can be all observations of all time, with past year, within past 6 months, etc.).
Regarding claim 8, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 1. Beaurepaire et al. further discloses:
wherein the machine-readable instruction that, when executed by the processor, causes the processor to provide the data set of lane marking positions comprises a machine-readable instruction that, when executed by the processor, causes the processor to provide the lane marking positions as offset measurements from a center of the roadway (¶ [0037] describing determining lane marking changes from a center of the road way 207a; see also Figure 2, Ref. No. 207a).
Regarding claim 9, Beaurepaire et al. discloses:
A non-transitory machine-readable medium (¶ [0006]) comprising instructions that, when executed by a processor (¶ [0109] memory and processor), cause the processor to:
train a [machine learning model] to represent lane marking position data based on a first data set of lane marking positions collected from vehicles traversing a roadway (¶ [0058] describing training the machine learning model on lane marking positions for a first fitness index);
provide a second data set of lane marking positions collected from the vehicles traversing the roadway as input to the [machine learning model] (¶¶ [0030, [0031], [0039] describing collecting data from a plurality of vehicles traveling a roadway that represent lane markings to input into a machine learning model);
update the [machine learning model] based on the second data set to obtain a second fitness metric (¶ [0057] describing updating or improving the machine learning model based on the data collected);
determine where a change in a lane marking on the roadway has occurred based on evaluating a comparison of the first fitness metric and the second fitness metric (¶ [0056] describing comparing the received data to the historical data in the machine learning model; ¶ [0058] describing identifying a change in the lane markings between the received data and the historical data); and
generate a notification of the change in the lane marking (¶¶ [0034], [0072] describing generating a notification of the change in the land marking).
Beaurepaire et al. does not expressly disclose that the learning model is a Gaussian Mixture model.
Zang et al., in the same field of endeavor, teaches use of a Gaussian Mixture model for identifying lane markings (¶ [0070], [0072] describing use of Gaussian model and Gaussian curves to determine lane markings).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Beaurepaire et al.’s invention to incorporate use of a Gaussian Mixture model as the machine learning model, as taught by Zang et al., with a reasonable expectation of success in determining changes in lane marking because a person of ordinary skill in the art would recognize the modification to be a mere simple substitution of one known learning model for another known learning model resulting in predictable results (Zang et al. at ¶ [0072]).
Regarding claim 10, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 9. Beaurepaire et al. further discloses:
wherein:
evaluating the comparison includes to identify the change based on a difference between the first fitness metric and the second fitness metric being greater than a threshold amount (¶ [0056] describing comparing the received data to the historical data in the machine learning model; ¶ [0036] describing the minimum and maximum threshold levels used to determine the probability of a change in the lane markings).
Regarding claim 11, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 9. Beaurepaire et al., as modified by Zang et al. further discloses:
wherein the machine-readable instructions further comprise a machine-readable instruction that, when executed by the processor, causes the processor to identify a type of the change in the lane marking on the roadway based on a shape change of Gaussian curves that define the GMM responsive to provision of the second data set as input to the GMM (Zang et al. at ¶ [0072] describing determining whether the lane markings match the Gaussian curves).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Beaurepaire et al.’s invention to incorporate use of a Gaussian Mixture model and the shape of Gaussian curves, as taught by Zang et al., with a reasonable expectation of success in determining changes in lane marking because a person of ordinary skill in the art would recognize the modification to be a mere simple substitution of one known learning model for another known learning model resulting in predictable results (Zang et al. at ¶ [0072]).
Regarding claim 12, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 11. Zang et al. further discloses:
wherein the machine-readable instruction that, when executed by the processor, causes the processor to identify the type of the change in the lane marking on the roadway comprises at least one of:
a machine-readable instruction that, when executed by the processor, causes the processor to identify a change in a number of lanes on the roadway based on the shape change of the Gaussian curves; or
a machine-readable instruction that, when executed by the processor, causes the processor to identify a shift in lanes on the roadway based on the shape change of the Gaussian curves (Zang et al. at ¶ [0072] describing determining whether the lane markings match the Gaussian curves, which represents a shift in the lanes on the roadway).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Beaurepaire et al.’s invention to incorporate use of a Gaussian Mixture model and Gaussian curves, as taught by Zang et al., with a reasonable expectation of success in determining changes in lane marking because a person of ordinary skill in the art would recognize the modification to be a mere simple substitution of one known learning model for another known learning model resulting in predictable results (Zang et al. at ¶ [0072]).
Regarding claim 13, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 9. Beaurepaire et al. further discloses:
wherein the machine-readable instructions further comprise a machine-readable instruction that, when executed by the processor, causes the processor to analyze metadata associated with the data set and the second data to determine a change to a type of the lane marking (¶ [0109] describing analyzing metadata associated with both data sets used to determine lane marking changes).
Regarding claim 14, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 9. Beaurepaire et al. further discloses:
wherein the machine-readable instruction that, when executed by the processor, causes the processor to provide the data set of lane marking positions comprises a machine-readable instruction that, when executed by the processor, causes the processor to provide a data set indicating multiple days of collected lane marking position data (¶ [0079] describing collecting lane marking positions over the course of hours, days, weeks, etc. and the historical lane marking data of the machine learning model can be all observations of all time, with past year, within past 6 months, etc.).
Regarding claim 15, Beaurepaire et al. discloses:
A method, comprising:
training a [machine learning model] to represent lane marking position data based on a first data set of lane marking positions collected from vehicles traversing a roadway (¶ [0058] describing training the machine learning model on lane marking positions for a first fitness index);
providing a second data set of lane marking positions collected from the vehicles traversing the roadway as input to the [machine learning model] (¶¶ [0030, [0031], [0039] describing collecting data from a plurality of vehicles traveling a roadway that represent lane markings to input into a machine learning model);
updating the [machine learning model] based on the second data set to obtain a second fitness metric (¶ [0057] describing updating or improving the machine learning model based on the data collected);
determine where a change in a lane marking on the roadway has occurred based on evaluating a comparison of the first fitness metric and the second fitness metric (¶ [0058] describing identifying a change in the lane markings between the received data and the historical data); and
generate a notification of the change in the lane marking (¶¶ [0034], [0072] describing generating a notification of the change in the land marking).
Beaurepaire et al. does not expressly disclose that the learning model is a Gaussian Mixture model.
Zang et al., in the same field of endeavor, teaches use of a Gaussian Mixture model for identifying lane markings (¶ [0070], [0072] describing use of Gaussian model and Gaussian curves to determine lane markings).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Beaurepaire et al.’s invention to incorporate use of a Gaussian Mixture model, as taught by Zang et al., with a reasonable expectation of success in determining changes in lane marking because a person of ordinary skill in the art would recognize the modification to be a mere simple substitution of one known learning model for another known learning model resulting in predictable results (Zang et al. at ¶ [0072]).
Regarding claim 16, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 15. Beaurepaire et al. further discloses:
wherein:
evaluating the comparison includes to identify the change based on a difference between the first fitness metric and the second fitness metric being greater than a threshold amount (¶ [0056] describing comparing the received data to the historical data in the machine learning model; ¶ [0036] describing the minimum and maximum threshold levels used to determine the probability of a change in the lane markings).
Regarding claim 17, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 15. Beaurepaire et al., as modified by Zang et al. further discloses:
wherein the machine-readable instructions further comprise a machine-readable instruction that, when executed by the processor, causes the processor to identify a type of the change in the lane marking on the roadway based on a shape change of Gaussian curves that define the GMM responsive to provision of the second data set as input to the GMM (Zang et al. at ¶ [0072] describing determining whether the lane markings match the Gaussian curves).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Beaurepaire et al.’s invention to incorporate use of a Gaussian Mixture model and the shape of Gaussian curves, as taught by Zang et al., with a reasonable expectation of success in determining changes in lane marking because a person of ordinary skill in the art would recognize the modification to be a mere simple substitution of one known learning model for another known learning model resulting in predictable results (Zang et al. at ¶ [0072]).
Regarding claim 18, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 17. Zang et al. further discloses:
wherein the machine-readable instruction that, when executed by the processor, causes the processor to identify the type of the change in the lane marking on the roadway comprises at least one of:
a machine-readable instruction that, when executed by the processor, causes the processor to identify a change in a number of lanes on the roadway based on the shape change of the Gaussian curves; or
a machine-readable instruction that, when executed by the processor, causes the processor to identify a shift in lanes on the roadway based on the shape change of the Gaussian curves (Zang et al. at ¶ [0072] describing determining whether the lane markings match the Gaussian curves, which represents a shift in the lanes on the roadway).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Beaurepaire et al.’s invention to incorporate use of a Gaussian Mixture model and Gaussian curves, as taught by Zang et al., with a reasonable expectation of success in determining changes in lane marking because a person of ordinary skill in the art would recognize the modification to be a mere simple substitution of one known learning model for another known learning model resulting in predictable results (Zang et al. at ¶ [0072]).
Regarding claim 19, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 15. Beaurepaire et al. further discloses:
wherein the machine-readable instructions further comprise a machine-readable instruction that, when executed by the processor, causes the processor to analyze metadata associated with the data set and the second data set to determine a change to a type of the lane marking (¶ [0109] describing analyzing metadata associated with both data sets used to determine lane marking changes).
Regarding claim 20, the combination of Beaurepaire et al. and Zang et al. renders obvious all limitations of claim 15. Beaurepaire et al. further discloses:
wherein the machine-readable instruction that, when executed by the processor, causes the processor to provide the data set of lane marking positions comprises a machine-readable instruction that, when executed by the processor, causes the processor to provide a data set indicating multiple days of collected lane marking position data (¶ [0079] describing collecting lane marking positions over the course of hours, days, weeks, etc. and the historical lane marking data of the machine learning model can be all observations of all time, with past year, within past 6 months, etc.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. U.S. Pub. No. 2023/0114215 to Tabak teaches use of Gaussian mixture models trained on lane marking data to determine lane boundaries (¶¶ [0045] – [0065]).
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.
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/JDH/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
3/31/26
1 It is unclear whether Applicant is arguing that there is an improvement to two different technologies: map updating v. crowdsourced lane-marking detection. Either way, as noted above there is no alleged improvement to that computer-based technology, nor is there support in the specification for such an improvement.
2 Examiner notes that the analysis of Claim 2 of Example 47 interprets continuous data as “having its plain mean of any data that is measured and can take on any number of possible values.” Therefore, for the purpose of a § 101 analysis there is no distinction between continuous and non-continuous data.