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, 11, and 20 have been amended.1
Response to Amendment
In response to Applicant’s amendments, Examiner maintains the claim objection with respect to claims 6 and 16; maintains the § 112(b) rejection of claims 6 and 16; maintains the § 101 rejection of claims 1-20; maintains the § 103 rejections of claims 1-20; and adds the below claim objection of claim 20.
Response to Arguments
Applicant’s arguments, see Remarks, filed 7/24/2026, with respect to the rejection(s) of claim 1 under Nayak and Shroff have been fully considered and are persuasive, in-part. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of U.S. Pub. No. 2020/0394838 to Bulan et al.
Specifically, Applicant’s argument regarding Shroff not disclosing augmenting an SD map layer with features to create an HD map layer is persuasive. The previous § 103 rejections are withdrawn on that basis alone.
Applicant's remaining arguments have been fully considered but they are not persuasive.
Rejection under § 101
Regarding Step 2A, Pring 1, Applicant argues that claim 1 does not recite an abstract idea because “[a]ggregating crowdsourced camera-derived position data from a fleet of vehicles, aligning it with aerial-imagery-derived road line data, and producing a new structured map layer in a map database cannot practically be performed in the human mind or with pen and paper.” Remarks at p. 1. Examiner respectfully disagrees.
First, there is no description in the present specification for crowdsourced data. Therefore, that aspect of the arguments fails to have support in the record.
Second, the only mention of “fleet” in the present specification is in ¶ [0018] where the invention is being distinguish from those systems that use fleets of vehicle. Specifically, it calls out the use of fleets of vehicles as a problem to be solved with the present invention. Therefore, Applicant’s argument contradicts their own specification.
Third, the claimed invention only requires gathering certain data and aggregating that data to create a standard definition map layer and a high definition map layer, which can all be done in the human mind.
As such, Applicant’s argument is unpersuasive and claim 1 recites an abstract idea.
Regarding Step 2A, Prong 2, Applicant argues that claim 1 provide an improvement to the technology because “[t]he specification discloses a concrete technical improvement: generating high-definition map data without specialized and expensive LiDAR-equipped vehicles by leveraging existing aerial imagery and data collected by conventional vehicles during ordinary navigation, enabling more frequent and comprehensive HD map updates at scale. The claim reflects this improvement through a specific ordered combination of steps culminating in the creation of a separate HD map layer that adds features the existing SD map layer lacks. That result is a tangible technical artifact produced by a particular technical method, not an abstract idea applied generically on a computer.” Remarks at p. 10.
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 generating high-definition map data. The technology is computer-based vehicle map data generation, 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). Applicant has not alleged an improvement to the computer system used to generate the centerline network, nor does the specification provide an improvement sufficient to improve the computer functionality.
Assuming, arguendo, that an improvement to the computing system itself it not required, Examiner notes that Applicant’s argument regarding the improvement being related to the lack of use of LiDAR and collecting data using conventional vehicles during ordinary navigation is not supported by the current claim 1. There is no limitation in claim 1 that prevents the aerial imagery and the vehicle detection data to be collected using LiDAR. LiDAR cameras are well-known in the art. Therefore, Applicant’s alleged improvement is not claimed in claim 1. However, simply using a different camera system than LiDAR is not likely an improvement as LiDAR has become prevalent in most vehicles comprising advanced driver assistance systems (ADAS).
As such, Applicant’s argument is unpersuasive and claim 1 does not provide a meaningful limitation that improves a technology or technical field.
Regarding Step 2B, Applicant argues that the claim amounts to significantly more than any judicial exception because “the cross-source alignment step and the separate HD layer creation step are not well-understood, routine, or conventional activities.” Remarks at p. 10. Applicant further states that the “Examiner has cited no evidence that they are...” Id. at p. 11.
Applicant is reminded that “Step 2B asks: Does the claim recite additional elements that amount to significantly more than the judicial exception? Examiners should answer this question by first identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)).” MPEP § 2106.05(B)(II) (emphasis in original).
As set forth below, the additional elements are “receiving aerial imagery of the geographic area …” and “receiving, from a plurality of computing devices in a corresponding plurality of vehicles, vehicle detection data,” which are well-understood, routine, and conventional activities and amount to mere data gathering. Furthermore, 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).
Applicant has identified elements that are not the additional elements that are examined during Step 2B. Instead, Applicant has identified elements of the judicial exception. Therefore, Applicant’s argument is moot.
As such, Applicant’s arguments are unpersuasive and claim 1 does not recite significantly more.
In conclusion, Applicant’s arguments with respect to claims 1 are unpersuasive for all the reasons set forth above. Applicant has not provided any independent arguments for claims 2-20.
Rejection under § 103
Applicant argues that Nayak does not align two independently identified datasets because Nayak does not treat road line data and map object positions as separate upstream datasets, does not identify them from separate source types, and does not align them into a unified upgrade artifact. Remarks at p. 12. Examiner respectfully disagrees. Nayak teaches receiving two independent sources of image data, one from an aerial view and one from a local view, and aligns them into a unified upgrade (Nayak at ¶¶ [0053] – [0055]; see also ¶¶ [0024] – [0027] describing sourcing data from aerial images and then sourcing data from local vehicles traveling in the area and aligning the two distinct datasets to update the map data). Furthermore, as pointed out by Applicant, Nayak discloses receiving aerial images and identifying road lines in at least ¶¶ [0063], [0065].
In conclusion, Applicant’s arguments with respect to claims 1, 11, and 20 are unpersuasive for all the reasons set forth above. Applicant has not provided any independent arguments for dependent claims 2-10 and 12-19.
Claim Objections
Claims 6 and 16 are objected to because of the following informalities: “L1” and “L2” should be fully defined prior to the use of an abbreviation.
Claim 20 is objected to because of the following informalities: Claim 20 has an incorrect claim status indicator. Claim 20 is indicated as “Original” despite containing amendments.
Appropriate correction is required.
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.
Claims 6 and 16 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.
Claims 6 and 16 recite “L1” and “L2” autonomous driving. It is unclear what L1 and L2 autonomous driving are intended to mean. There is no description in the specification identifying the capabilities for different levels of autonomous driving, or identifying a particular standard for autonomous driving accepted in the industry. For example, the current standard for autonomous driving is the J3016 SAE International Levels of Autonomous Driving. However, Applicant has not referenced that standard. Furthermore, ¶¶ [0003] and [0067] of the present specification reference a L6, of which the SAE standard does not have.2 As such, Applicant is not referencing the SAE standard and it is unclear what standard for levels of autonomous driving is being referenced. Therefore, claims 6 and 16 are indefinite.
Examiner notes that should Applicant amend the specification to identify a standard associated with the referenced L1-L6, it would be considered new matter.
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 1-10 recite a computer-implemented method, therefore claims 1-10 are a process, which is within at least one of the four statutory categories.
Claims 11-19 recite a non-transitory computer readable medium storing a program, therefore claims 11-19 is a machine, which is within at least one of the four statutory categories.
Claim 20 recites a system, therefore claims 11-19 is a machine, 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 computer-implemented method for producing a high-definition map layer for a map of a geographic area in a map database, the map including a standard definition map layer, the computer-implemented method comprising:
receiving aerial imagery of the geographic area;
identifying, using the aerial imagery, road line data representing road lines in the geographic area;
receiving, from a plurality of computing devices in a corresponding plurality of vehicles, vehicle detection data indicating positions of map objects in the geographic area, the vehicle detection data being derived by the plurality of computing devices from camera data of the plurality of computing devices;
determining the positions of the map objects from the vehicle detection data;
generating an upgrade data object representing the road lines and map objects by aligning the road line data identified from the aerial imagery and the determined positions of the map objects identified from the vehicle detection data; and
augmenting the map with the upgrade data object representing the road lines and map object, the upgrade data object creating the high-definition map layer of the geographic area in the map, and wherein the high-definition map layer is a separate layer from the standard definition map layer, and the high-definition map layer comprises features absent from the standard definition map layer, the standard definition map layer and high-definition map layer represent the geographic area at different levels of fidelity.
These limitations, as drafted, is a method that, under its broadest reasonable interpretation, covers performance of the limitation as certain mental processes and/or mathematical concepts. That is, nothing in the claim elements preclude the steps from practically being performed as in the mind (or on paper). For example, “identifying…road line data…, “determining the positions…,” “generating an upgrade data object…,” and “augmenting the map with the upgrade data object” encompass a human mentally identifying road line data, determining positions of map objects, aligning the road lines and map objects, and augmenting a map to increase the fidelity to a higher fidelity. Thus, the claim recites at least one abstract idea. The other independent claims of similar scope of claim 1 also recite at least one abstract idea.
101 Analysis - Step 2A, Prong 2
Regarding Prong 2 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. 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 computer-implemented method for producing a high-definition map layer for a map of a geographic area in a map database, the map including a standard definition map layer, the computer-implemented method comprising:
receiving aerial imagery of the geographic area;
identifying, using the aerial imagery, road line data representing road lines in the geographic area;
receiving, from a plurality of computing devices in a corresponding plurality of vehicles, vehicle detection data indicating positions of map objects in the geographic area, the vehicle detection data being derived by the plurality of computing devices from camera data of the plurality of computing devices;
determining the positions of the map objects from the vehicle detection data;
generating an upgrade data object representing the road lines and map objects by aligning the road line data identified from the aerial imagery and the determined positions of the map objects identified from the vehicle detection data; and
augmenting the map with the upgrade data object representing the road lines and map object, the upgrade data object creating the high-definition map layer of the geographic area in the map, and wherein the high-definition map layer is a separate layer from the standard definition map layer, and the high-definition map layer comprises features absent from the standard definition map layer, the standard definition map layer and high-definition map layer represent the geographic area at different levels of fidelity.
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 identifying road line data, determining positions of map objects, aligning the road lines and map objects, and augmenting a map to increase the fidelity to a higher fidelity, 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).
Moreover, receiving aerial imagery and receiving vehicle detection data are mere insignificant extra solution activities. MPEP 2106.05(d)
Specifically with respect to claims 11-20, the additional elements of a memory and processor 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 non-transitory computer readable medium storing a program identifying road line data, determining positions of map objects, aligning the road lines and map objects, and augmenting a map to increase the fidelity to a higher fidelity 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 “receiving aerial imagery of the geographic area…” and “receiving, from a plurality of computing devices in a corresponding plurality of vehicles, vehicle detection data indicating positions of map objects in the geographic area, the vehicle detection data being derived by the plurality of computing devices from camera data of the plurality of computing devices” amounts to nothing more than insignificant extra solution activities, such as data gathering.
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 “receiving aerial imagery…” and “receiving…vehicle detection data…” is well-understood, routine, and conventional activities that involve mere data gathering. 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, 11, and 20 are patent ineligible under 35 U.S.C. 101.
Dependent claims 2-10 and 12-19 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 2 and 12 recite additional data gathering (“receiving telemetry data…”), claims 3-7 and 13-17 are directed toward additional aspects of the judicial exception (“map layer is produced without using light detection and ranging…,” “objects are road signs,” “increase the fidelity…,” “clustering the objects,” “collect vehicle data…[when] the geographic area is less than a desired fidelity”), and claims 8, 9, 18, and 19 are directed to merely applying the judicial exception to a generic computer (“apply…a machine learning model…,” “applying one or more processing functions…”). Therefore, dependent claims 2-10 and 12-19 are not patent eligible under the same rationale as provided for in the rejection of claims 1, 11, and 20.
Therefore, claims 1-20 are ineligible under 35 USC § 101.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 4, 8, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2024/0087092 to Nayak et al. (previously of record) in view of U.S. 2020/0003897 to Shroff et al. (previously of record) and U.S. Pub. No. 2020/0394838 to Bulan et al. (previously of record).
Regarding claim 1, Nayak et al. discloses:
A computer-implemented method for producing a map layer for a map of a geographic area in a map database, the map including a standard definition map layer, the computer-implemented method comprising:
receiving aerial imagery of the geographic area (Figure 5, Step 710; ¶ [0053] describing receiving aerial images of the area);
identifying, using the aerial imagery, road line data representing road lines in the geographic area (Figure 5, Step 720; ¶ [0053] describing identifying features in the images; ¶ [0061] describing that the features identified in the images can be road line data);
receiving, from a plurality of computing devices in a corresponding plurality of vehicles, vehicle detection data indicating positions of map objects in the geographic area, the vehicle detection data being derived by the plurality of computing devices from camera data of the plurality of computing devices (¶ [0054] describing receiving position of map object from a plurality of vehicles traveling through the area);
determining the positions of the map objects from the vehicle detection data (¶ [0054] describing determining the positions of the map objects);
generating an upgrade data object representing the road lines and map objects by aligning the road line data identified from the aerial imagery and the determined positions of the map objects identified from the vehicle detection data (¶ [0055] describing generating upgrade data representing road lines and map objects by aligning the road line data and the position of the map objects); and
augmenting the map with the upgrade data object representing the road lines and map object, the upgrade data object creating the map layer of the geographic area in the map (¶ [0054] describing updating the map data based on the received image data and map object data).
Nayak et al. does not expressly disclose that it is a high-definition map that is being produced, or wherein the standard definition map layer and high-definition map layer represent the geographic area at different levels of fidelity, that the high-definition layer comprises features absent the standard definition layer, or augmenting a standard definition map to create a high definition map.
Shroff et al., in the same field of endeavor, teaches producing high-definition maps where the standard definition layer and the high-definition layer represent the geographic area at different levels of fidelity, and the high-definition layer comprises features absent the standard definition layer (¶¶ [0014], [0017], [0025], [0026], [0031] describing producing a low resolution, or standard definition, map layer and a high resolution, or high-definition, map layer, where the high resolution map layer represents a higher level of detail of the map, i.e., comprises features absent the standard definition layer).
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 Nayak et al.’s invention to incorporate producing a high-definition layer where the standard definition layer and the high-definition layer represent the geographic area at different levels of fidelity, where the high-definition layer comprises features absent the standard definition layer, as taught by Shroff et al., with a reasonable expectation of success in providing the map system with the ability to select between a low resolution map layer or a high resolution map layer depending on prioritized resolution (¶¶ [0013], [0014]).
Bulan et al., in the same field of endeavor, teaches augmenting standard definition map layer with features to create high definition map layers (¶ [0024] describing creating HD maps using SD maps, as well as aerial imagery).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further modify Nayak et al.’s invention to incorporate augmenting SD map layers with features to generate HD map layer, as taught by Bulan et al., with a reasonable expectation of success in generating map features based on aerial data and telemetry without the need for specialized driving mapping vehicles typically used to generate map features (Bulan et al. at ¶ [0023]).
Regarding claim 2, the combination of Nayak et al., Shroff et al., and Bulan et al. renders obvious all the limitations of claim 1. Bulan et al. further discloses:
receiving telemetry data from the plurality of computing devices in the corresponding plurality of vehicles, the telemetry data comprising positions, headings, and velocities of the plurality of vehicles in the geographic area; wherein producing the high-definition map layer comprises aligning the telemetry data with the road line data and clustered positions of the map objects (¶ [0024] describing the use of aerial images and telemetry data to generating high-definition maps; ¶ [0052] describing using telemetry data that includes position, heading, and velocity in the alignment process to remove discrepancies).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further modify Nayak et al.’s invention to incorporate use of telemetry data comprising positions, headings, and velocities to align the road line data and clustered positions of map objects, as taught by Bulan et al., with a reasonable expectation of success in correcting discrepancies in the map data (Bulan et al. at ¶ [0052]), and providing a technique for generating map features without the need for specialized driving mapping vehicles (Bulan et al. at ¶ [0023]).
Regarding claim 3, the combination of Nayak et al., Shroff et al., and Bulan et al. renders obvious all the limitations of claim 1. Nayak et al. further discloses:
wherein the high-definition map layer is produced without using light detection and ranging (LiDAR) data (¶¶ [0053], [0054] describing using cameras to produce the map layer).
Regarding claim 4, the combination of Nayak et al., Shroff et al., and Bulan et al. renders obvious all the limitations of claim 1. Nayak et al. further discloses:
wherein the map objects are road signs (¶ [0063] describing the map objects as road signs).
Regarding claim 8, the combination of Nayak et al., Shroff et al., and Bulan et al. renders obvious all the limitations of claim 1. Nayak et al. further discloses:
wherein the vehicle detection data is determined by: applying, using a computing device of the plurality of computing devices, a machine learned model to the camera data of that computing device to identify the map object; and identifying a geolocation of the map object using telemetry data of the computing device (¶ [0056], [0061] describing applying a machine learning model to the camera data to identify the object and its location).
Regarding claim 9, the combination of Nayak et al., Shroff et al., and Bulan et al. renders obvious all the limitations of claim 1. Nayak et al. further discloses:
increasing a fidelity of the aerial imagery by applying one or more processing functions before identifying road lines in the aerial imagery (¶ [0061] describing using processes, including algorithms and machine learning models, to increase the fidelity of the aerial images by capturing newer images of the objects, which includes road lines).
Claims 5, 6, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Nayak et al., Shroff et al., and Bulen at al., as applied to claim 1 above, and further in view of U.S. Pub. No. 2023/0258472 to Ashman et al. (previously of record).
Regarding claim 5, the combination of Nayak et al., Shroff et al., and Bulan et al. renders obvious all the limitations of claim 1.
Neither Nayak et al., Shroff et al., nor Bulan et al. expressly discloses wherein producing the high-definition map layer is in response to a mapping system determining to increase the fidelity of the map relative to the fidelity of the standard definition map layer.
Ashman et al., in the same field of endeavor, teaches wherein producing the high-definition map layer is in response to a mapping system determining to increase the fidelity of the map relative to the fidelity of the standard definition map layer (¶ [0046] describing that the fidelity, or detail, of the map can be scaled up or down based on the needs of the system or by request of a user).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further modify Nayak et al.’s invention to incorporate producing the high-definition layer in response to a mapping system determining to increase the fidelity relative to a standard definition, as taught by Ashman et al., with a reasonable expectation of success in providing a user or the system the ability to request higher resolution map layers based on the desired level of autonomous driving (Ashman et al. at ¶ [0028]).
Regarding claim 6, the combination of Nayak et al., Shroff et al., and Bulan et al. renders obvious all the limitations of claim 1.
Neither Nayak et al., Shroff et al., nor Bulan et al. expressly disclose wherein the standard definition map layer has a first fidelity necessary for L1 autonomous driving and the high-definition map layer has a second fidelity higher than the first fidelity necessary for L2 or higher autonomous driving.
Ashman et al., in the same field of endeavor teaches using different fidelity map layers depending on the level of autonomous driving to be performed by the vehicle, which includes a standard definition map for driver assistance level and a more detailed, or high-definition, map for levels 3-5 (¶ [0028] describing requesting additional map data because level of automation, i.e. user desires to operate at a L3 autonomous mode, but the current map is only suitable for a L1 mode; see also ¶¶ [0138] – [0140] describing the requirements for map to operate at L3-L5; ¶ [0046] describing that the fidelity, or detail, of the map can be scaled up or down based on the needs of the system or by request of a user; NOTE: Subject to the §112(b) rejection above).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further modify Nayak et al.’s invention to incorporate a first fidelity for L1 driving and a second higher fidelity for L2 or higher autonomous driving, as taught by Ashman et al., with a reasonable expectation of success in providing a user or the system the ability to request higher resolution map layers based on the desired level of autonomous driving (Ashman et al. at ¶ [0028]).
Regarding claim 10, the combination of Nayak et al., Shroff et al., and Bulan et al. renders obvious all the limitations of claim 1.
Neither Nayak et al., Shroff et al., nor Bulan et al. expressly disclose instructing the plurality of computing devices to collect vehicle detection data in the geographic area when a fidelity of a map representing the geographic area is less than a desired fidelity.
Ashman et al., in the same field of endeavor, teaches requesting map data in a geographical region when a fidelity of a map is less than a desired fidelity (¶ [0028] describing requesting additional map data because level of automation, i.e. user desires to operate at a L3 autonomous mode, but the current map is only suitable for a L1 mode; see also ¶¶ [0138] – [0140] describing the requirements for map to operate at L3-L5; ¶ [0046] describing that the fidelity, or detail, of the map can be scaled up or down based on the needs of the system or by request of a user).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further modify Nayak et al.’s invention to incorporate requesting the high-definition layer in response to a mapping system determining to increase the fidelity relative to a standard definition, as taught by Ashman et al., with a reasonable expectation of success in providing a user or the system the ability to request higher resolution map layers based on the desired level of autonomous driving (Ashman et al. at ¶ [0028]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Nayak et al., Shroff et al., and Bulan et al., as applied to claim 1 above, and further in view of U.S. Pub. No. 2020/0201890 to Viswanathan (previously of record).
Regarding claim 7, the combination of Nayak et al., Shroff et al., and Bulan et al. renders obvious all the limitations of claim 1.
Neither Nayak et al., Shroff et al., nor Bulan et al. expressly disclose wherein determining the positions of the map objects from the vehicle detection data comprises clustering the objects across the vehicle detection data to identify consistent map objects.
Viswanathan, in the same field of endeavor, teaches determining the positions of the map objects from the vehicle detection data comprises clustering the objects across the vehicle detection data to identify consistent map objects (¶ [0047] describing clustering, categorizing, the detected map objects).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further modify Nayak et al.’s invention to incorporate clustering the map objects, as taught by Viswanathan, with a reasonable expectation of success in allowing the detected features to only be correlated with map features of the same semantic attribute category, thereby substantially reducing the amount of data that the system requires (Viswanathan at ¶ [0047]).
Claims 11-19 list all the same elements of claims 1-9, but the additional elements of a non-transitory computer-readable storage medium storing computer program instructions and one or more processor (¶ [0009] describing the computer processor, memory, and non-transitory computer-readable medium). Therefore, the supporting rationale of the rejection to claims 1-9 applies equally as well to claims 11-19.
Claim 20 list all the same elements of claim 1, but the additional elements of a non-transitory computer-readable storage medium storing computer program instructions and one or more processor (¶ [0009] describing the computer processor, memory, and non-transitory computer-readable medium). Therefore, the supporting rationale of the rejection to claim 1 applies equally as well to claim 20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Pub. No. 20200072610 to Hofmann et al. teaches receiving aerial imagery of road features to generate maps (¶¶ [0064] – [0079]);
U.S. Pub. No. 2023/0349716 to Wang et al. teaches generating HD maps by adding features to SD maps (¶¶ [0028] – [0039]).
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
8/5/26
1 Applicant stated in their Remarks that claims 6 and 16 were canceled. However, the claims filed on 7/24/2026 still have claims 6 and 16 present.
2 https://www.sae.org/standards/j3016_202104-taxonomy-definitions-terms-related-driving-automation-systems-road-motor-vehicles
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