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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 August 14, 2026, has been entered.
Status of Claims
This Office action is in response to the amendments filed on August 14, 2026. Claims 97-119 are currently pending, Claims 97, 103-104 being amended.
Response to Amendments
In response to amendments, submitted August 14, 2026, the Examiner withdraws the previous 35 U.S.C. 103 rejections.
Response to Arguments
Applicant’s arguments, filed August 14, 2026, with respect to the rejections of Claims 97-119 under Shashua, in view of Schnieder and Janssen, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Shashua, in view of Schnieder, Penilla, and Janssen.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on February 26, 2026, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
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 97-115 and 117-119 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2017/0010618 A1, to Shashua, et al (hereinafter referred to as Shashua; previously of record), in view of U.S. Patent Publication No. 2018/0180435 A1, to Schneider (hereinafter referred to as Schneider; previously of record), and further in view of U.S. Patent Publication No. 2017/0197617 A1, to Penilla, et al (hereinafter referred to as Penilla, newly of record).
As per Claim 97, and similarly for Claim 103, Shashua discloses the features of a system for navigating a host vehicle, the system comprising (e.g. Paragraphs [0008], [0165]; where the system may use crowd sourced data for autonomous vehicle navigation), the system comprising
at least one processor comprising circuitry and a memory, wherein the memory includes instructions (e.g. Paragraph [0375]; where sparse map (800) may be stored on a non-transitory computer-readable medium provided onboard the vehicle, and a processor (e.g., processing unit (110) may access sparse map (800) stored in the storage device in order to generate navigational instructions for guiding the autonomous vehicles as it traverse a road segment), that when executed by the circuitry cause the of least one processer to:
receive an image acquired by at least one camera onboard the host vehicle as the host vehicle traverses a road segment (e.g. Paragraphs [0096], [0326], [0371], [0530], [0866]; where a road feature is captured by a camera onboard the vehicle; and where the system (100) may use two or more image capture devices (122 and 124) in providing navigation assistance, and processing unit (110) may combine the processed information derived from each of the image capture devices and determine visual indicators, vehicle location, etc.);
detect a representation of at least one traffic sign in the acquired image (e.g. Paragraph [0330]; where the image analysis module may include instructions for detecting a set of features within the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, and any other feature associated with an environment of a vehicle);
access a crowd-sourced map generated based on drive information collected from a plurality of vehicles that previously traversed the road segment (e.g. Paragraphs [0438], [0616]; where the processing unit (110) may access one or more local maps corresponding to the road segment being traversed; where each vehicle may communicate with a remote server, and the server may process the collected data to generate an autonomous vehicle road navigation model, and the server may then transmit the collected data to the autonomous vehicle road navigation model or the update to the model to other vehicles that travel on the road segment at later times),
the crowd-sourced map including a plurality of drivable paths associated with road segment (e.g. Paragraphs [0017], [0380], [0509]; Figure 11C; where the sparse map may include representations of a plurality of target trajectories for guiding autonomous driving or navigation along a road segment (i.e. a plurality of drivable paths), and a target trajectory may be associated with a single lane of the common road segment, and the road navigation model may include a plurality of target trajectories, each associated with a separate lane; and where additional trajectories may be stored to representing intended paths of travel for vehicles in one or more lanes), wherein
the crowd-sourced map stores relevancy information indicating, for each individual drivable path of the plurality of drivable paths, whether the traffic sign is relevant to the individual drivable path (e.g. Paragraphs [0338], [0376], [0382], [0465], [0492]; Figures 11B, 22, 28; where the relevant local map and relevant landmarks in the vicinity of the vehicles may be stored in memory; and where landmarks are determined, identified, and associated with a corresponding road segment relevant to a location in which the vehicle is traveling (i.e. relevant to a path or plurality of driving paths)), wherein
the indication that the traffic sign is relevant to the individual drivable path is based on a distance in the crowd-sourced map between the individual drivable path and the traffic sign (e.g. Paragraphs [0892]-[0893], [0897]-[0899], [0931]; Figures 74, 77B, 82B-C; where the processor determines an offset between the vehicle and the at least one recognized landmark, and determines a lane assignment of the vehicle based on the indicator of the lateral offset distance between the vehicle and the landmark and a lane edge closest to the landmark to determine that a current lane assignment of the vehicle is correct (i.e. determines the distance to the relevant traffic signs based on the current path of the vehicle)) and
on motion characteristics of one or more of the plurality of vehicles within a threshold distance ‘…’, the motion characteristics indicating that the one or more of the plurality of vehicles slowed or changed heading direction within the threshold distance of the traffic sign (e.g. Paragraphs [0333], [0341], [0404], [0458]-[0459], [0966]; where determination of the steering direction may be based on additional cues, such as motion of a forward vehicle, and positions of vehicles forward of the autonomous vehicle; and where the processing unit (110) may perform an analysis of motion patterns relative to the vehicle associated with other vehicles and pedestrians based on position and speed information of nearby vehicles when the vehicles are traveling a common road segment based on landmark, road geometry, and vehicle motion information received from the other vehicles; and when a navigational maneuver is detected, the server may determine updates from the same location or a nearby location (e.g., in “pulling” such updates from vehicles at the location or nearby location);
determine, based on the accessed crowd-sourced map, a drivable path of the plurality of paths along which the host vehicle is traveling (e.g. Paragraphs [0380], [0402], [0509]; Figure 11C; where the sparse map may include representations of a plurality of target trajectories for guiding autonomous driving or navigation along a road segment (i.e. a plurality of drivable paths); and where additional trajectories may be stored to representing intended paths of travel for vehicles in one or more lanes; and where the target trajectory may represent a preferred path of a host vehicle (i.e. at least one drivable path));
determine that the at least one traffic sign detected in the acquired image is relevant of the drivable path along which the host vehicle is traveling when the relevancy information indicates that the at least one traffic sign detected in the acquired image is relevant to the drivable path along which the host vehicle is traveling (e.g. Paragraphs [0067], [0428], [0535], [0579]; Figures 28, 33b, 40, 50; where the recognized landmark may be a stop line, traffic light, stop sign, etc.; where landmarks that are directly relevant to driving may include traffic signs, lane markings, traffic lights, stop lines, etc., and a tag of relevant landmarks derived from GPS coordinates and positioning may be stored and used to determine which data is relevant for navigation); and
in response to a determination that the at least one traffic sign detected in the acquired image is relevant to the drivable path along which the host vehicle is traveling, cause the host vehicle of take at least one navigational action relative to the at least one traffic sign (e.g. Paragraphs [0330], [0333]; where the system may cause one or more navigational responses in vehicle, such as a turn, a lane shift, a change in acceleration, and the like).
Schneider, in the same field of endeavor, more explicitly teaches the features of indicating that the traffic sign is relevant to at least a first path of the plurality of drivable paths; and determine that the at least one traffic sign detected in the acquired image is relevant of the drivable path along which the host vehicle is traveling.
Schneider teaches a method for determining relevant road data proximate to a vehicle’s trajectory, where the driver assistance system detects road signs on the main and secondary routes, but ignores actions associated with traveling on the secondary route (i.e. ignores irrelevant data) (e.g. Paragraphs [0011], [0032]; Claim 4).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the adaptive navigation system in Shashua, with the feature of determining where vehicles stopped in the system of Schneider in order to provide the most relevant information to the driver based on its current trajectory and location (see at least Paragraphs [0008] and [0010] of Schneider).
Shashua, in view of Schneider, fails to teach every feature of wherein the indication that the traffic sign is relevant to the individual drivable path is based on a distance in the crowd-sourced map between the individual drivable path and the traffic sign and on motion characteristics of one or more of the plurality of vehicles within a threshold distance of the traffic sign, the motion characteristics indicating that the one or more of the plurality of vehicles slowed or changed heading direction within the threshold distance of the traffic sign.
However, Penilla, in a similar field of endeavor, teaches a method for connected vehicle communication for intersections, where the system determines which of the traffic lights corresponds to the subject vehicle’s direction of travel, and relevant connected objects in relation to the subject vehicle are identified, which may include vehicles in front of, behind, or beside the subject vehicle, and traffic lights that are at the intersection in the direction of travel of the subject vehicle; and where the system determines the relevant connected objects can include only the traffic lights that may be relevant to the subject vehicle and other vehicles close enough to the intersection and to the subject vehicle (i.e., a threshold distance from the same intersection/ traffic signs); and the system may determine whether the other vehicle is proceeding through the intersection or turning or braking, or has its turn signal on indicating its intention to turn left, based on changes in state of at least one relevant connected object, such as a traffic light or a turn signal of one or more relevant connected objects near the intersection (i.e., determines the traffic sign/light is relevant to the path) (e.g. Paragraphs [0256]-[0261], [0265], [0297]; Figures 28, 29C, 30).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the adaptive navigation system in Shashua, in view of Schneider, with the feature of determining relevant objects and vehicles near an intersection in the system of Penilla, in order to provide the most relevant information to the driver (see at least Paragraphs [0263] of Penilla).
As per Claim 104, Shashua discloses the features of a non-transitory computer readable medium containing instructions, that when executed by at least one processor comprising circuitry and a memory, wherein the memory includes instructions (e.g. Paragraph [0375]; where sparse map (800) may be stored on a non-transitory computer-readable medium provided onboard the vehicle, and a processor (e.g., processing unit (110) may access sparse map (800) stored in the storage device in order to generate navigational instructions for guiding the autonomous vehicles as it traverse a road segment), that when executed by the at least one processor, cause the of least one processer to perform operations comprising:
receiving an image acquired by at least one camera onboard the host vehicle as the host vehicle traverses a road segment (e.g. Paragraphs [0326], [0371]; where a road feature is captured by a camera onboard the vehicle; and where the system (100) may use two or mor image capture devices (122 and 124) in providing navigation assistance, and processing unit (110) may combine the processed information derived from each of the image capture devices and determine visual indicators, vehicle location, etc.);
detecting a representation of at least one traffic sign in the acquired image (e.g. Paragraph [0330]; where the image analysis module may include instructions for detecting a set of features within the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, and any other feature associated with an environment of a vehicle);
accessing a crowd-sourced map generated based on drive information collected from a plurality of vehicles that previously traversed the road segment (e.g. Paragraphs [0438], [0616]; where the processing unit (110) may access one or more local maps corresponding to the road segment being traversed; where each vehicle may communicate with a remote server, and the server may process the collected data to generate an autonomous vehicle road navigation model, and the server may then transmit the collected data to the autonomous vehicle road navigation model or the update to the model to other vehicles that travel on the road segment at later times),
the crowd-sourced map including a plurality of drivable paths associated with road segment (e.g. Paragraphs [0017], [0380], [0509]; Figure 11C; where the sparse map may include representations of a plurality of target trajectories for guiding autonomous driving or navigation along a road segment (i.e. a plurality of drivable paths), and a target trajectory may be associated with a single lane of the common road segment, and the road navigation model may include a plurality of target trajectories, each associated with a separate lane; and where additional trajectories may be stored to representing intended paths of travel for vehicles in one or more lanes), wherein
the crowd-sourced map stores relevancy information indicating, for each individual drivable path of the plurality of paths, whether the traffic sign is relevant to at least the individual drivable path (e.g. Paragraphs [0067], [0428], [0535], [0579]; Figures 28, 33b, 40, 50; where the recognized landmark may be a stop line, traffic light, stop sign, etc.; where landmarks that are directly relevant to driving may include traffic signs, lane markings, traffic lights, stop lines, etc., and a tag of relevant landmarks derived from GPS coordinates and positioning may be stored and used to determine which data is relevant for navigation), wherein
the indication that the traffic sign is relevant to the individual drivable path is based on a distance in the crowd-sourced map between the individual drivable path and the traffic sign (e.g. Paragraphs [0892]-[0893], [0897]-[0899], [0931]; Figures 74, 77B, 82B-C; where the processor determines an offset between the vehicle and the at least one recognized landmark, and determines a lane assignment of the vehicle based on the indicator of the lateral offset distance between the vehicle and the landmark and a lane edge closest to the landmark to determine that a current lane assignment of the vehicle is correct (i.e. determines the distance to the relevant traffic signs based on the current path of the vehicle)) and
on motion characteristics of one or more of the plurality of vehicles within a threshold distance ‘…’, the motion characteristics indicating that the one or more of the plurality of vehicles slowed or changed heading direction within the threshold distance of the traffic sign (e.g. Paragraphs [0333], [0341], [0404], [0458]-[0459], [0966]; where determination of the steering direction may be based on additional cues, such as motion of a forward vehicle, and positions of vehicles forward of the autonomous vehicle; and where the processing unit (110) may perform an analysis of motion patterns relative to the vehicle associated with other vehicles and pedestrians based on position and speed information of nearby vehicles when the vehicles are traveling a common road segment based on landmark, road geometry, and vehicle motion information received from the other vehicles; and when a navigational maneuver is detected, the server may determine updates from the same location or a nearby location (e.g., in “pulling” such updates from vehicles at the location or nearby location);
determining, based on the accessed crowd-sourced map, a drivable path of the plurality of paths along which the host vehicle is traveling (e.g. .g. Paragraphs [0380], [0402], [0509]; Figure 11C; where the sparse map may include representations of a plurality of target trajectories for guiding autonomous driving or navigation along a road segment (i.e. a plurality of drivable paths); and where additional trajectories may be stored to representing intended paths of travel for vehicles in one or more lanes; and where the target trajectory may represent a preferred path of a host vehicle (i.e. at least one drivable path));
determining that the at least one traffic sign detected in the acquired image is relevant of the drivable path along which the host vehicle is traveling when the relevancy information indicates that the at least one traffic sign detected in the acquired image is relevant to the drivable path along which the host vehicle is traveling (e.g. Paragraphs [0067], [0428], [0535], [0579]; Figures 28, 33b, 40, 50; where the recognized landmark may be a stop line, traffic light, stop sign, etc.; where landmarks that are directly relevant to driving may include traffic signs, lane markings, traffic lights, stop lines, etc., and a tag of relevant landmarks derived from GPS coordinates and positioning may be stored and used to determine which data is relevant for navigation); and
in response to a determination that the at least one traffic sign detected in the acquired image is relevant to the drivable path along which the host vehicle is traveling, causing the host vehicle of take at least one navigational action relative to the at least one traffic sign (e.g. Paragraphs [0330], [0333]; where the system may cause one or more navigational responses in vehicle, such as a turn, a lane shift, a change in acceleration, and the like).
Schneider, in the same field of endeavor, more explicitly teaches the features of indicating that the traffic sign is relevant to at least a first path of the plurality of drivable paths; and determine that the at least one traffic sign detected in the acquired image is relevant of the drivable path along which the host vehicle is traveling. Schneider teaches a method for determining relevant road data proximate to a vehicle’s trajectory, where the driver assistance system detects road signs on the main and secondary routes, but ignores actions associated with traveling on the secondary route (i.e. ignores irrelevant data) (e.g. Paragraphs [0011], [0032]; Claim 4).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the adaptive navigation system in Shashua, with the feature of determining where vehicles stopped in the system of Schneider in order to provide the most relevant information to the driver based on its current trajectory and location (see at least Paragraphs [0008] and [0010] of Schneider).
Shashua, in view of Schneider, fails to teach every feature of wherein the indication that the traffic sign is relevant to the individual drivable path is based on a distance in the crowd-sourced map between the individual drivable path and the traffic sign and on motion characteristics of one or more of the plurality of vehicles within a threshold distance of the traffic sign, the motion characteristics indicating that the one or more of the plurality of vehicles slowed or changed heading direction within the threshold distance of the traffic sign.
However, Penilla, in a similar field of endeavor, teaches a method for connected vehicle communication for intersections, where the system determines which of the traffic lights corresponds to the subject vehicle’s direction of travel, and relevant connected objects in relation to the subject vehicle are identified, which may include vehicles in front of, behind, or beside the subject vehicle, and traffic lights that are at the intersection in the direction of travel of the subject vehicle; and where the system determines the relevant connected objects can include only the traffic lights that may be relevant to the subject vehicle and other vehicles close enough to the intersection and to the subject vehicle (i.e., a threshold distance from the same intersection/ traffic signs); and the system may determine whether the other vehicle is proceeding through the intersection or turning or braking, or has its turn signal on indicating its intention to turn left, based on changes in state of at least one relevant connected object, such as a traffic light or a turn signal of one or more relevant connected objects near the intersection (i.e., determines the traffic sign/light is relevant to the path) (e.g. Paragraphs [0256]-[0261], [0265], [0297]; Figures 28, 29C, 30).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the adaptive navigation system in Shashua, in view of Schneider, with the feature of determining relevant objects and vehicles near an intersection in the system of Penilla, in order to provide the most relevant information to the driver (see at least Paragraphs [0263] of Penilla).
As per Claim 98, and similarly for Claims 105 and 110, Shashua, in view of Schneider and Penilla, teaches the features of Claims 97, 103, and 104, respectively, and Schneider further teaches the features of wherein the memory further includes instructions that, when executed by the circuitry, cause the at least one processor to: in response to a determination that the at least one traffic sign detected in the acquired image is not relevant of a drivable path along which the host vehicle is traveling, cause the host vehicle to forego a navigational response relative to the at least one traffic sign.
Schneider teaches a method for determining relevant road data proximate to a vehicle’s trajectory, where the driver assistance system detects road signs on the main and secondary routes, but ignores actions associated with traveling on the secondary route (i.e. ignores irrelevant data) (e.g. Paragraphs [0011], [0032]; Claim 4).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the adaptive navigation system in Shashua, with the feature of determining where vehicles stopped in the system of Schneider in order to provide the most relevant information to the driver.
As per Claim 99, and similarly for Claim 106 and 111, Shashua, in view of Schneider and Penilla, teaches the features of Claims 97, 103, and 104, respectively, and Shashua further discloses the features of
wherein the at least one traffic sign is a stop sign (e.g. Paragraphs [0382], [0391], [0392]; where map data may include data relating to a plurality of predetermined landmarks (820) associated with a particular road segment; and where the landmarks may include traffic signs, directional signs, general signs), and
the at least one navigational action includes braking the host vehicle (e.g. Paragraphs [0316], [0333], [0741]; where the system (100) may provide control signals to one or more of the throttling system (220), the braking system (230), and steering system (240) to navigate the vehicle; and where the vehicle may automatically control the braking, acceleration, or steering; and where braking of the vehicle may be initiated a certain distance from recognized landmarks, such as a stop line, a traffic light, a stop sign, a sharp curve, etc.,);
As per Claim 100, and similarly for Claims 107 and 112, Shashua, in view of Schneider and Penilla, teaches the features of Claims 97, 103, and 104, respectively, and Shashua further discloses the features of
wherein the at least one traffic sign is a yield sign (e.g. Paragraphs [0382], [0391], [0392]; where map data may include data relating to a plurality of predetermined landmarks (820) associated with a particular road segment; and where the landmarks may include traffic signs, directional signs, general signs), and
the at least one navigational action includes braking the host vehicle and changing a heading direction of the host vehicle (e.g. Paragraphs [0316], [0333], [0741]; where the vehicle may automatically control the braking, acceleration, or steering; and where braking of the vehicle may be initiated a certain distance from recognized landmarks, such as a stop line, a traffic light, a stop sign, a sharp curve, etc., and the system may cause one ore mor navigational responses such as a turn, lane shift, and change in acceleration).
As per Claim 101, and similarly for Claims 108 and 113, Shashua, in view of Schneider and Penilla, teaches the features of Claims 97, 103, and 104, respectively, and Shashua further discloses the features of wherein the drivable path is stored in the crowd-sourced map as a three-dimensional spline (e.g. Paragraph [0370], [0380]; where the sparce map (800) may include representations of a plurality of target trajectories (810) for guiding the autonomous driving or navigation along a road segment, and may be stored as three-dimensional splines).
As per Claim 102, and similarly for Claims 109 and 114, Shashua, in view of Schneider and Penilla, teaches the features of Claims 97, 103, and 104, respectively, and Shashua further discloses the features of wherein the plurality of drivable paths stored in the crowd- sourced map are determined by aggregating driving paths followed by the plurality of vehicles that previously traversed the road segment (e.g. Paragraphs [0401], [0404]; where the representation of the target trajectory include in sparse map (800) may be an aggregation of two or more reconstructed trajectories of prior traversals of vehicles along the same road segment).
As per Claim 115, Shashua, in view of Schneider and Penilla, teaches the features of Claim 97, and Schneider further teaches the features of wherein the relevancy information further indicates that the traffic sign is not relevant to at least a second path of the plurality of drivable paths and wherein the memory further includes instructions that when executed by the circuitry cause the at least one processor to determine, based on the relevancy information that the traffic sign is not relevant to the second path.
Schneider teaches a method for determining relevant road data proximate to a vehicle’s trajectory, where the driver assistance system detects road signs on the main and secondary routes, but ignores actions associated with traveling on the secondary route (i.e. ignores irrelevant data) (e.g. Paragraphs [0011], [0032]; Claim 4).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the adaptive navigation system in Shashua, with the feature of determining where vehicles stopped in the system of Schneider in order to provide the most relevant information to the driver based on its current trajectory and location (see at least Paragraphs [0008] and [0010] of Schneider).
As per Claim 117, Shashua, in view of Schneider and Penilla, teaches the features of Claim 97, and Shashua further discloses the features of wherein the relevancy information is determined based on a comparison of positions of the traffic sign in the crowd- sourced map with positions of one or more road features in the crowd- sourced map (e.g. Paragraphs [0072], [0371], [0772], [0814]; where the road features each have a digital signature, which is compared to the signature of the road feature that is captured by a camera onboard a vehicle traveling along the same road segment at a subsequent time, to determine if the image data associated with the road feature is locally unique in the area; and the processing unit (110) may determine the location of the vehicle by comparing the determined local feature and the stored road signature (i.e. compares position of a road feature against landmarks and the digital signature to determine position information)).
As per Claim 118, Shashua, in view of Schneider and Penilla, teaches the features of Claim 97, and Shashua further discloses the features of wherein the relevancy information is determined based on a proximity of the traffic sign in the crowd-sourced map to the one or more road features in the crowd-sourced map (e.g. Paragraphs [0371], [0785]-[0786]; where the combination of known lengths for specific segments (e.g., typically close to an intersection) together with statistics regarding consistent segment lengths and spacing are used to determine the signatures for road features to determine if the signatures match the local feature (i.e. proximity to road features); and where the road features each have a digital signature, which is compared to the signature of the road feature that is captured by a camera onboard a vehicle traveling along the same road segment at a subsequent time, to determine if the image data associated with the road feature is locally unique in the area; and the processing unit (110) may determine the location of the vehicle by comparing the determined local feature and the stored road signature).
As per Claim 119, Shashua, in view of Schneider and Penilla, teaches the features of Claim 97, and Shashua further discloses the features of wherein the distance in the crowd-sourced map between the individual driving path and the traffic sign is a lateral distance between the traffic sign in the crowd- sourced map and the individual drivable path (e.g. Paragraphs [0371], [0898]; where the processor determines a lane assignment of the vehicle based on an indicator of the lateral offset distance between the vehicle and the at least one recognized landmark, based on a lateral distance from the recognized landmark to a lane edge (i.e. drivable path) closest to the recognized landmark, to any lane edges present on the road, to a target trajectory associated with a road segment, or to multiple target trajectories associated with the road segment, etc.; and the determined indicator of lateral offset distance between the recognized landmark and the host vehicle may be compared to any of these quantities, among others, and then used to determine a current lane assignment based on one or more arithmetic and/or trigonometric calculations).
Claim 116 is rejected under 35 U.S.C. 103 as being unpatentable over Shashua, in view Schneider and Penilla, as applied to Claim 97 above, and further in view of German Patent Publication No. 102008043155 A1, to Janssen, et al (hereinafter referred to as Janssen; previously of record).
As per Claim 116, Shashua, in view of Schneider and Penilla, teaches the features of Claim 97, and Shashua further teaches the features of wherein the plurality of drivable paths includes at least a first drivable path associated with a first lane of travel along the road segment and a second drivable path associated with a second lane of travel along the road segment, the first lane of travel and the second lane of travel being adjacent to each other and being associated with the same direction of travel (e.g. Figures 76, 77A-B; where a first and second driving lane are determined and presented, along with relevant signs and road features on each side and around the vehicle).
The combination of Shashua, in view of Schneider and Penilla, fails to teach every feature of wherein the relevancy information indicates that the at least one traffic sign detected in the acquired image is relevant to the first drivable path and is not relevant to the second drivable path.
However, Janssen, in a similar field of endeavor, teaches a method for displaying road sign information to a driver, where a displayed traffic sign is determined to be relevant based on the lane in which the driver is traveling, or is determined to be relevant for another lane (e.g. Paragraphs [0002], [0016]; Figures 2, 3).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the adaptive navigation system in Shashua, in view of Schneider and Penilla, with the feature of determining which sign is relevant to the traveling lane of the vehicle, in order to improve the representation of information to the driver (see at least Paragraphs [0002]-[0003] of Janssen).
Conclusion
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/MERRITT LEVY/Examiner, Art Unit 3663
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