DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/25/2025 has been entered.
Status of Claims
Claims 1-24 and 33-36 of U.S. Application No. 17/773,676 filed on 09/15/2022. Examiner filed a non-final rejection on 08/27/2024.
Applicant filed remarks and amendments on 02/26/2025. Claims 1, 7, 9, 14, 17, and 23 were amended. Claims 1-24 and 33-36 were examined. Examiner filed a final rejection on 05/28/2025.
Applicant filed an RCE on 11/25/2025. Claims 1, 9 and 17 were amended. Claims 1-24 and 33-36 are presently pending and presented for examination.
Response to Arguments
Regarding the claim rejections under 35 USC 102: Applicant's arguments filed 11/25/2025 with respect to Shashua et al. (US 9623905 B2) have been fully considered but they are not persuasive.
Regarding claims 1 and 17, The Applicant argues that The interpretation of “linked” in the Office Action is not consistent with the way that term would be understood by one of skill in the art, as per the applicant’s specification [0044]-[0045], [0062]. The Office Action equates “linked” with the fact that two optical landmarks appear sequentially along a road, but there is no indication that there is an established link between these landmarks other than appearing in a data set. Shashua does nothing to associate these landmarks with each other or permit them to be analyzed in the claimed manner
However, The Examiner respectfully disagrees this argument is not persuasive, Shashua teaches linked landmarks as landmarks that are associated through their placement along the same road segment and integrated into a predetermined road model trajectory for navigation. For example, Shashua describes “a sequence of landmarks may include a stop sign, a speed limit sign, and a yield sign.” [Col.111 line 13-15] (as described in the patent section discussing solution two for unique landmarks; see also Fig. 11A and description), where these landmarks collectively identify the road segment and anchor the trajectory. The system uses this sequence to determine vehicle position and direction relative to the road model, inherently linking the landmarks via their shared road context and sequential utility in localization. This association goes beyond mere sequential appearance in a data set, as the landmarks are processed together to define and navigate the segment between them.
The Applicant also argues that the statistical analysis recited in Claim 1 is not met by Shashua. The cited passage in column 115-116 concerns only the refinement of a single landmark’s position estimate using averaged or median values derived from multiple image-based observations of that same landmark. This passage does not disclose or suggest any statistical analysis of vehicle traversals between two landmarks, any analysis of traversal frequencies or co-occurrence rates, any conditional probability of progressing from one landmark to another, or any other statistical analysis that might disclose the limitations of Claim 1. Shashua’s teachings are directed to map refinement and visual landmark localization, not traversal-based statistical modeling.
However, The Examiner respectfully disagrees this argument is not persuasive, Shashua teaches statistical analysis of vehicle traversals between linked landmarks through the generation of the predetermined road model trajectory, which aggregates data from multiple vehicle traversals. Specifically, “To average a cluster of trajectories, server 1230 may select a reference frame of an arbitrary trajectory C 0 . For all other trajectories (C 1 , . . . , Cn), server 1230 may find a rigid transformation that maps Ci to C 0 , where i=1, 2, . . . , n, where n is a positive integer number, corresponding to the total number of trajectories included in the cluster. Server 1230 may compute a mean curve or trajectory in the C 0 reference frame.” [Col.86 line 6-13] (as described in the patent description of averaging trajectories), where this averaging creates a representative path spanning the portion between landmarks. This constitutes a statistical analysis (averaging to reduce variance) of historical vehicle traversals between the linked first and second landmarks, enabling prediction of traversal along that portion. The single-landmark refinement in col. 115-116 (“The measured position or location of the landmark may behave like a random variable, and hence may be averaged to improve accuracy.” [Col.116 line 61-64]) complements but does not limit the broader trajectory-level processing.
The Applicant also argues that the system described in Shashua provides no opportunity for the use of statistical analysis of vehicle traversals between the linked first and second reference landmarks. Shashua’s landmarks are typically recognized objects—traffic signs, lane markings, posts, billboards, and similar structures—that are intentionally placed for universal visibility. Any vehicle traveling along the same roadway will inevitably view the same optical landmarks in the same order. Thus, any vehicle that observes a first such optical landmark will inevitably observe a second landmark farther along the same road segment. Therefore, there is no statistical analysis disclosed in Shashua because every vehicle observes the same sequence of landmarks with no variation in them. One of skill in the art would not modify the teachings of Shashua to use predictions based on a statistical analysis because no such statistical analysis is needed or disclosed in Shashua.
However, The Examiner respectfully disagrees this argument is not persuasive, Shashua explicitly accounts for variations in traversals despite fixed landmarks, due to sensor noise, path deviations, and observation differences, requiring statistical processing. For instance, landmark positions are refined using “The position of the landmark may be determined by averaging the position measurements detected, collected, or received by sensor systems on different vehicles 1205 - 1225 through multiple drives.” [Col.82 line 38-41] (as described in the patent description of landmark position), and trajectories are similarly averaged after alignment to create a reliable model. The system predicts traversal of the road portion between landmarks using this statistically refined trajectory, as the averaged path represents the consensus from historical variations in vehicle behavior between those points. The description addresses variation handling via statistics on observations, while the averaging process extends this principle to trajectory paths between aligned landmarks (“In some embodiments, the landmarks may define an arc length matching between different drives, which may be used for alignment of trajectories with lanes.” [Col.86 line 14-16]), showing statistical analysis is both disclosed and necessary.
Regarding claim 9, The Applicant argues that the Office Action does not identify any disclosure in Shashua in which a portion of the road surface inclusively extending between two linked landmarks is compared with reference data. Amended Claim 9 distinguishes Shashua for at least this additional reason.
However, The Examiner respectfully disagrees this argument is not persuasive, Shashua teaches comparing vehicle parameters with reference data for the road portion between linked landmarks. The processor is programmed to “The location of the vehicle along a target trajectory may be adjusted based on the distance to the landmark and the landmark’s known location (stored in the road model or sparse map 800 ).” [Col.86 line 64-67] (as described in the patent description of vehicle localization using landmarks), where the trajectory extends between sequential landmarks (as in sequences) and is derived from averaged historical data. This comparison localizes the vehicle on the road surface portion between the landmarks.
The Applicant argues that the second amended limitation is likewise not met. Shashua controls vehicle behavior based on lane geometry, sign recognition, and environmental semantics extracted from images, and not as recited in Claim 9. Nothing in Shashua teaches or suggests controlling a system of a vehicle based at least in part on the use of a reference road profile extending between linked reference landmarks
However, The Examiner respectfully disagrees this argument is not persuasive, Shashua teaches control “The at least one processor 1715 may cause at least one navigational maneuver (e.g., steering such as making a turn, braking, accelerating, passing another vehicle, etc.) by vehicle 1205 based on the received autonomous vehicle road navigation model or the updated portion of the model.” [Col.88 line 45-50] (as described in the patent description of navigational actions), where the trajectory profile—averaged from traversals—extends between linked landmarks and guides steering.
Regarding claim 33, The Applicant argues that Shashua does not disclose “determining a path of travel that each vehicle takes relative to the plurality of reference landmarks to identify links between the plurality of reference landmarks” and “generating a mesh of the plurality of reference landmarks and the links extending between the plurality of reference landmarks.”
However, The Examiner respectfully disagrees this argument is not persuasive, Shashua processes multiple vehicle paths, aligns them “The sequences of the landmarks may be aligned taking into account possibly missing landmarks in the recordings from some of the vehicles.” [Col.111 line 33-35] to identify connections, and generates a sparse map as interconnected trajectories, forming a mesh-like structure of landmarks and linking paths.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-24 and 33-36 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shashua et al. (US 9623905 B2).
Regarding claims 1, 15 and 33, Shashua discloses A method of localizing a vehicle, the method comprising: sensing one or more parameters associated with a road surface of a road the vehicle is traversing (“The disclosed systems and methods may enable autonomous vehicle navigation (e.g., steering control) with low footprint models, which may be collected by the autonomous vehicles themselves without the aid of expensive surveying equipment. To support the autonomous navigation (e.g., steering applications), the road model may include the geometry of the road, its lane structure, and landmarks that may be used to determine the location or position of vehicles along a trajectory included in the model. Generation of the road model may be performed by a remote server that communicates with vehicles travelling on the road and that receives data from the vehicles. The data may include sensed data, trajectories reconstructed based on the sensed data, and/or recommended trajectories that may represent modified reconstructed trajectories. The server may transmit the model back to the vehicles or other vehicles that later travel on the road to aid in autonomous navigation” [Col.76 line 1-18]);
identifying a first reference landmark on the road the vehicle has encountered using the sensed one or more parameters(“For example, the disclosed systems and methods may provide navigation based on recognized landmarks, align a vehicle's tail for navigation, allow a vehicle to navigate road junctions, allow a vehicle to navigate using local overlapping maps, allow a vehicle to navigate using a sparse map, navigate based on an expected landmark location, autonomously navigate a road based on road signatures, provide forward navigation based on a rearward facing camera, navigate based on a free space determination, navigate in snow, provide autonomous vehicle speed calibration, determine lane assignment based on a recognized landmark location, and use super landmarks as navigation aids.” [Col.117 line 20-35]);
identifying a second reference landmark on the road linked to the first reference landmark (“Sparse map 800 may also include representations of other road-related features associated with geographic region 1111. For example, sparse map 800 may also include representations of one or more landmarks identified in geographic region 1111. Such landmarks may include a first landmark 1150 associated with stop line 1132, a second landmark 1152 associated with stop sign 1134, a third landmark associated with speed limit sign 1154, and a fourth landmark 1156 associated with hazard sign 1138.” [Col.73 line 14-22]);
predicting that the vehicle will traverse a portion of the road surface extending between the first reference landmark and the second reference landmark (“Such landmarks may be used, for example, to assist an autonomous vehicle in determining its current location relative to any of the shown target trajectories, such that the vehicle may adjust its heading to match a direction of the target trajectory at the determined location.” [Col.73 line 14-22] and “The predicted path may be generated by processing images of the environment ahead of the autonomous vehicle and detecting lane, or other road layout, markings” [Col.125 ln 54-65]) based on a statistical analysis of vehicle traversals between the linked first and second reference landmarks (“Processor 2930 may use the averaged position as the refined position. In some embodiments, processor 2930 may calculate a median value of the measured position and the at least one previously acquired position (e.g., a plurality of previously acquired positions), and use the median value as the refined position. Other statistical parameters that may be obtained from the measured position and the plurality of previously acquired positions may be used as the target position. Process 3000 may update the location of the landmark stored in a map with the refined position (step 3330). For example, processor 2930 may replace the position stored in the map with the refined position. When new position data is received, processor 2930 may repeat steps 3320 and 3330 to refine the position of the landmark stored in the map, thereby increasing the accuracy of the position of the landmark.” [Col. 115, 116 ln 56-67, ln 1-5]);
and controlling a system of the vehicle based at least in part on one or more selected from the group of a feature of the second reference landmark and a reference road profile extending between the first reference landmark and the second reference landmark (“The method may include transmitting a control signal specifying the steering angle to a steering system of the vehicle. Determining the heading for the vehicle may include determining a previous location of the vehicle relative to the road junction based on the intersection of the directional indicators for the two or more landmarks; and determining the heading based on the previous location and the current location.” [Col.12 ln 32-48]) .
Regarding claims 2, 10, 18 and 35, Shashua discloses The method of claim 1, further comprising comparing the one or more parameters to a reference road profile to identify the first reference landmark (“At step 544, processing unit 110 may analyze multiple frames of images to determine whether objects in the set of candidate objects represent vehicles and/or pedestrians. For example, processing unit 110 may track a detected candidate object across consecutive frames and accumulate frame-by-frame data associated with the detected object (e.g., size, position relative to vehicle 200, etc.). Additionally, processing unit 110 may estimate parameters for the detected object and compare the object's frame-by-frame position data to a predicted position.” [Col.54 line 56-65]).
Regarding claims 3, 11 and 19, Shashua discloses The method of claim 2, further comprising obtaining the reference road profile (“At step 4906, processing unit 110 may analyze the received sparse map and the at least one image of the environment of vehicle 200. For example, processing unit 110 may execute monocular image analysis module 402 to analyze one or more images, as described in further detail in connection with FIGS. 5B-5D. By performing the analysis, processing unit 110 may detect a set of features within the set of images, for example, one or more landmarks, such as landmarks 1134, 1136, and 1138. As discussed earlier, landmarks may include one or more traffic signs, arrow markings, lane markings, dashed lane markings, traffic lights, stop lines, directional signs, reflectors, landmark beacons, lampposts, a change is spacing of lines on the road, signs for businesses, and the like. Furthermore, processing unit 110 may analyze the sparse map to determine that an object in one or more images is a recognized landmark. For example, processing unit 110 may compare the image of the object to data stored in the sparse map. Based on the comparison, the image processor 190 may determine whether or not the object is a recognized landmark. Processing unit 110 may use recognized landmarks from captured image data of the environment and/or GPS data to determine a position of vehicle 200. Processing unit 110 may then determine a position of vehicle 200 relative to a target trajectory of the sparse map.” [Col.149 line 46-67]).
Regarding claims 4, 12, 20 and 34, Shashua discloses The method of claim 3, wherein the reference profile comprises a mesh of a plurality of reference landmarks located on the road surface, and wherein each reference landmark of the mesh is linked to at least one other reference landmark in the mesh (“The plurality of landmarks 820 may be identified and stored in sparse map 800 at any suitable spacing. In some embodiments, landmarks may be stored at relatively high densities (e.g., every few meters or more). In some embodiments, however, significantly larger landmark spacing values may be employed. For example, in sparse map 800, identified (or recognized) landmarks may be spaced apart by 10 meters, 20 meters, 50 meters, 100 meters, 1 kilometer, or 2 kilometers. In some cases, the identified landmarks may be located at distances of even more than 2 kilometers apart. Between landmarks, and therefore between determinations of vehicle position relative to a target trajectory, the vehicle may navigate based on dead reckoning in which it uses sensors to determine its ego motion and estimate its position relative to the target trajectory. Because errors may accumulate during navigation by dead reckoning, over time the position determinations relative to the target trajectory may become increasingly less accurate. The vehicle may use landmarks occurring in sparse map 800 (and their known locations) to remove the dead reckoning-induced errors in position determination. In this way, the identified landmarks included in sparse map 800 may serve as navigational anchors from which an accurate position of the vehicle relative to a target trajectory may be determined. Because a certain amount of error may be acceptable in position location, an identified landmark need not always be available to an autonomous vehicle. Rather, suitable navigation may be possible even based on landmark spacings, as noted above, of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers, or more. In some embodiments, a density of 1 identified landmark every 1 km of road may be sufficient to maintain a longitudinal position determination accuracy within 1 m. Thus, not every potential landmark appearing along a road segment need be stored in sparse map 800.” [Col.66 line 65-67 & Col.67 line 1-31]).
Regarding claims 5, 13 and 20, Shashua discloses The method of claim 1, further comprising determining a lateral position of the vehicle on the road surface based at least partly on a location of the first reference landmark (“In some embodiments, a system for determining a lane assignment for an autonomous vehicle along a road segment may include at least one processor programmed to: receive from a camera at least one image representative of an environment of the vehicle; analyze the at least one image to identify at least one recognized landmark; determine an indicator of a lateral offset distance between the vehicle and the at least one recognized landmark; and determine a lane assignment of the vehicle along the road segment based on the indicator of the lateral offset distance between the vehicle and the at least one recognized landmark.” [Col.24 line 14-24]).
Regarding claims 6 and 22, Shashua discloses The method of claim 1, further comprising continuing to sense the one or more parameters and comparing the one or more parameters to a reference road profile extending between the first reference landmark and the second reference landmark (“The plurality of landmarks 820 may be identified and stored in sparse map 800 at any suitable spacing. In some embodiments, landmarks may be stored at relatively high densities (e.g., every few meters or more). In some embodiments, however, significantly larger landmark spacing values may be employed. For example, in sparse map 800, identified (or recognized) landmarks may be spaced apart by 10 meters, 20 meters, 50 meters, 100 meters, 1 kilometer, or 2 kilometers. In some cases, the identified landmarks may be located at distances of even more than 2 kilometers apart. Between landmarks, and therefore between determinations of vehicle position relative to a target trajectory, the vehicle may navigate based on dead reckoning in which it uses sensors to determine its ego motion and estimate its position relative to the target trajectory.” [Col.66 line 65 - Col.67 line 32], see also [Col.86 line 49 - Col.87 line 3] and [Col.149 line 46 – Col.150 line 3]).
Regarding claims 7, 14 and 23, Shashua discloses The method of claim 1, wherein the system of the vehicle is controlled prior to the vehicle encountering the second reference landmark. (“In some embodiments of the system, the navigational maneuver may be based on a recognized landmark identified in the at least one environmental image. The information relating to the user input may include information specifying at least one of a degree of a turn of the vehicle, an amount of an acceleration of the vehicle, and an amount of braking of the vehicle. The control system may include at least one of a steering control, an acceleration control, and a braking control. The navigational situation information may include one or more images captured by a camera onboard the vehicle. The user input may include at least one of braking, steering, or accelerating.” [Col.27 line 36-61] and “The determination of the lane assignment may be further based on a predetermined road model trajectory associated with the road segment. The at least one recognized landmark may include a first recognized landmark on a first side of the vehicle and a second recognized landmark on a second side of the vehicle and wherein determination of the lane assignment of the vehicle along the road segment is based on a first indicator of lateral offset distance between the vehicle and the first recognized landmark and a second indicator of lateral offset distance between the vehicle and the second recognized landmark.” [Col.24 ln 40-57]).
Regarding claims 8, 24 and 36, Shashua discloses The method of claim 7, further comprising determining a level of confidence associated with the prediction, wherein the level of confidence is determined based at least in part on a total number of reference landmarks that are linked to the first reference landmark; and wherein the system of the vehicle is controlled based at least in part on the determined level of confidence(“The plurality of landmarks 820 may be identified and stored in sparse map 800 at any suitable spacing. In some embodiments, landmarks may be stored at relatively high densities (e.g., every few meters or more). In some embodiments, however, significantly larger landmark spacing values may be employed. For example, in sparse map 800, identified (or recognized) landmarks may be spaced apart by 10 meters, 20 meters, 50 meters, 100 meters, 1 kilometer, or 2 kilometers. In some cases, the identified landmarks may be located at distances of even more than 2 kilometers apart. Between landmarks, and therefore between determinations of vehicle position relative to a target trajectory, the vehicle may navigate based on dead reckoning in which it uses sensors to determine its ego motion and estimate its position relative to the target trajectory.” [Col.66 line 65 - Col.67 line 32], see also [Col.86 line 49 - Col.87 line 3], [Col.110 line 31-33], [Col.122 line 29-53] and [Col.150 line 4-46]).
Regarding claims 9 and 17, Shashua discloses A method of localizing a vehicle, the method comprising: (“Consistent with disclosed embodiments, the system can store information obtained during autonomous navigation (or regular driver-controlled navigation) for use in later traversals along the same road.” (col. 79, lines 53-57)).
sensing one or more parameters associated with a road surface of a road the vehicle is traversing: (Sensing occurs via image capture of road surface features. “the analysis of the at least one image may include identifying at least one tire track in the snow. The analysis of the at least one image may 55 include identifying a change of light across a surface of the snow.” (col. 21, lines 53-56)).
identifying a first reference landmark on the road the vehicle has encountered based at least partly on the sensed one or more parameters: (The processor identifies landmarks from images. “analyze the at least one image to identify at least one recognized landmark” (col. 9, lines 12-13). Landmarks include road surface-associated features like “a stop line, a stop sign, a speed limit sign”).
continuing to sense the one or more parameters as the vehicle traverses the road surface: (Continuous sensing via multiple images during travel. “receive a plurality of images representative of an environment of the vehicle from an image capture device included in the vehicle as the vehicle travels along the road” (col. 8, lines 40-44)).
comparing the one or more parameters with information related to a portion of the road surface inclusively extending between the first reference landmark and at least one other reference landmark linked with the first reference landmark to determine a position of the vehicle on the road surface: (Position is determined by comparing to the trajectory extending between landmarks. “determine a current location of the vehicle relative to a predetermined road model trajectory associated with the road segment based, at least in part, on a predetermined location of the recognized landmark” (col. 5, lines 21-24); The trajectory spans portions between sequential/ linked landmarks in the sparse map, where “The sparse map may include one or more recognized landmarks. The recognized landmarks may be spaced apart in the sparse map at a rate of no more than 0.5 per kilometer. The recognized landmarks may be spaced apart in the sparse map at a rate of no more than 1 per 15 kilometer. The recognized landmarks may be spaced apart in the sparse map at a rate of no more than 1 per 100 meters. The sparse map may have a data density of no more than 100 kilobytes per kilometer.” (col. 15, Lines 10-20)).
controlling a system of the vehicle based at least in part on one or more features selected from the group consisting of a feature of the at least one reference landmark and a feature of a reference road profile extending between the first reference landmark and the at least one other reference landmark linked to the first reference landmark: (Control uses trajectory features between landmarks. “determine an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined location.” (col. 17, lines 40-44)). Features include landmark types like (“a traffic sign, an arrow marking, a lane marking” (col. 8, lines 50-51)), and the profile is the trajectory extending between them.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMED ALKIRSH whose telephone number is (703) 756-4503. The examiner can normally be reached M-F 9:00 am-5:00 pm EST.
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/AA/Examiner, Art Unit 3668 /Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668