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 .
Response to Arguments
1. Applicant's arguments filed 24 November 2025 have been fully considered but are not persuasive. The new limitations are disclosed by at least Ghose as detailed in the rejection below. Arguments concerning the new claim limitations are also addressed in the revised rejection below.
Claim Rejections - 35 USC § 112
2. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
3. Claims 1-4 and 6-21 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.
The claim limitation “the log data identifies a first pullover location selected by the autonomous vehicle” is indefinite. It is unclear how log data can identify a location, that has already been selected by the autonomous vehicle. Or how passive data can identify something, rather than be used to identify something.
The examiner will assume that “the log data identifies a first pullover location selected by the autonomous vehicle” means “the log data includes a first pullover location selected by the autonomous vehicle”. As best understood by the examiner, the claims will be treated on the merits in this office action.
Claim Rejections - 35 USC § 103
4. 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 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.
5. Claims 1-2, 16-17, and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Ghose et al. (US 2018/0304926 A1) in view of Della Penna (US 20190220011 A1).
Ghose and Della Penna are considered analogous to the claimed invention because they are in the same field of vehicle navigation and control (see MPEP 2141.01(a)).
Regarding claims 1 and 20-21, Ghose teaches a method comprising:
accessing, by one or more processors, pre-stored map information used to control an autonomous vehicle, the pre-stored map information identifying a plurality of predetermined types of regions of interest (see at least Ghose [0094] “Moreover, in certain embodiments, a cost map is generated by assigning values to different types of polygons and by rasterizing the various cost polygons. For example, in various embodiments, polygons pertaining to the shape of the tracked objects, keep-clear zones, intersection lanes, and the like are utilized in the analysis.”, [0044], [0077], [0073])
identifying, by the one or more processors, from log data, a set of potential pullover locations within a predetermined distance of a destination (see at least Ghose [0005] “determining, by a processor using the data, when the autonomous vehicle is proximate a destination; and, when the autonomous vehicle is proximate the destination: identifying, by the processor using the data, a plurality of potential parking locations proximate the destination”, [0084] “In certain embodiments, the possible parking locations pertain to curbside pick-ups and/or drop-off locations for a passenger to exit from and enter the vehicle 10.”, [0068], Fig. 7, [0093]),
and wherein the log data identifies a first pullover location selected by the autonomous vehicle (see at least Ghose Fig. 7, [0093] first parking situation/ pullover location shown in Fig. 7: 700, [0005], [0084]);
and determining, by the one or more processors, which ones of the potential pullover locations of the set include or at least partially overlap with one or more of the plurality of predetermined types of regions of interest identified in the pre-stored map information (see at least Ghose Fig. 7, [0093] first and second situations/ pullover locations shown in Fig. 7 at least partially overlap with types of regions of interest such as traffic flow and lane regions; [0073] “the possible parking locations 502 are analyzed with respect to a number of different factors, such as respective distances 512 between each parking location 502 and the destination 505, the detected objects 510 and their proximity to the respective parking locations 502, detected traffic flows with respect to each parking location 502, the lane widths 514 of the respective lanes 506, 508 (e.g., the lane(s) in proximity to the possible parking location), applicable local parking laws and regulations, and respective ride comfort measures with respect to the different possible parking locations 502”); and
selecting, by the one or more processors, a second pullover location from the at least one of the potential pullover locations of the set based on the determining (see at least Ghose Fig. 7, [0093] second parking situation/ pullover location shown in Fig. 7: 720 selected based on higher score due to overlap with curb region and lack of overlap with traffic flow and lane regions, [0005] “selecting, by the processor using the data, a selected parking location of the potential parking locations based on the respective score of each of the potential parking locations”).
Ghose does not teach but Della Penna teaches wherein the log data is collected by the autonomous vehicle as the autonomous vehicle approaches the destination (see at Della Penna [0026] “Event recorder 156 may be configured to “data mine,” thereby collecting data and information from a variety of sensors in sensor platform 121, as well as derived data generated by logic, algorithms, or processes of autonomy controller 150, such as localization data, perception data (e.g., object recognition and classification data), trajectory data, and physical vehicle data (e.g., steering angles, braking pressures, etc”, [0031] “event recorder 156 may be configured to detect any type of event and record any type or amount of event data, according to various examples”, [0047] “event recorder 256 may be configured to modify a path of travel associated with a parking spot to, for example, modify an orientation or position of the vehicle as it travels”, abstract, [0004], [0017], [0054], [0056]);
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ghose view of Della Penna to collect log data as the autonomous vehicle approaches the destination as disclosed in Della Penna. At the time the invention was filed, one of ordinary skill in the art would have been motivated to modify the invention in this way in order to select pullover locations using the most accurate and up-to-date log data.
Regarding claim 19, Della Penna teaches wherein the adjusting or updating of the autonomous vehicle control software changes at least one of a success rate or a failure rate of the autonomous vehicle control software (see at least Della Penna [0066] “As an example of an exception or deviation from path planning rules, an analysis of data gathered by autonomous vehicle logic at an autonomous vehicle may be transmitted for analysis at event-adaptive computing platform 509. A result may be an update to software that improves the onboard autonomous vehicle logic”, wherein improving the software would prevent the same failure from occurring).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ghose in view of Della Penna to include “wherein the adjusting or updating of the autonomous vehicle control software changes at least one of a success rate or a failure rate of the autonomous vehicle control software” as disclosed in Della Penna. The motivation for making this modification to Ghose is the same as that above in the rejection of claim 1.
Regarding claim 2, Ghose further teaches comparing, by the one or more processors, the second pullover location to the first pullover location in order to evaluate the first pullover location (see at least Ghose Fig. 7, [0093] first and second parking situations/ pullover locations shown in Fig. 7 are compared based on score and overlap with types of regions, and first pullover location is evaluated independently and in comparison to second pullover location).
Ghose does not teach but Della Penna teaches adjusting or updating autonomous vehicle control software used to select a first pullover location identified by the log data based on the comparison (see at least Della Penna [0061] “After the analysis, updated software or autonomous logic may be transmitted back to an autonomous vehicle to update the autonomous logic.”, [0066] “As an example of an exception or deviation from path planning rules, an analysis of data gathered by autonomous vehicle logic at an autonomous vehicle may be transmitted for analysis at event-adaptive computing platform 509. A result may be an update to software that improves the onboard autonomous vehicle logic”).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ghose view of Della Penna to include “adjusting or updating autonomous vehicle control software used to select a first pullover location identified by the log data based on the comparison” as disclosed in Della Penna. At the time the invention was filed, one of ordinary skill in the art would have been motivated to modify the invention in this way in order to adjust the vehicle software to overcome deficiencies and improve safety and reliability of autonomous driving (see at least Della Penna [0004]-[0005] “As one example, known techniques for detecting and correcting deficiencies in autonomous operation of a fleet of test are generally limited to a minimal set of actions that are usually downloaded upon return to a central location at which data may be analyzed… Conventionally, manual manipulation of locally-gathered operational data is performed to identify areas of improvement, diagnostic situations, and is time, labor, and resource-intensive… Thus, what is needed is a solution for implementing autonomous control functions to resolve anomalies associated with control of driverless vehicles, without the limitations of conventional techniques.”, [0023] “Event-adaptive computing platform 109 may include centralized or distributed hardware and/or software configured to analyze numerous events associated with numerous autonomous vehicles 120 to identify patterns, deficiencies (whether functional or structural), or any other areas of improving navigation and propulsion of autonomous vehicle 120 in a safe, reliable manner.”, [0027] “in some examples, event recorder 156 and event-adaptive computing platform 109, in combination, may “learn” which subset of characteristics may be modified to improve, for example, reliable autonomous vehicle operation.”)
Regarding claim 16, Ghose teaches wherein the plurality of predetermined types of regions of interest includes areas where a vehicle can stop or park (see at least Ghose Fig. 7, [0093], Fig. 5 such as curb regions).
Regarding claim 17, Ghose teaches wherein the plurality of predetermined types of regions of interest includes no stopping or standing zones (see at least Ghose Fig. 7, [0093], Fig. 5 such as traffic flow or lane regions).
7. Claims 3-6, 13-15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ghose in view of Della Penna, and further in view of Puri et al. (US 2020/0011671 A1).
Puri is considered analogous to the claimed invention because they are in the same field of vehicle navigation and control (see MPEP 2141.01(a)).
Regarding claim 3-4 and 6, Ghose does not teach but Puri teaches evaluating, by the one or more processors, each potential pullover location in the set of potential pullover locations to determine whether any potential pullover locations can be categorized in a particular one of a plurality of ranked buckets (see at least Puri [0058] “As a first such example, a parking probability data source 308 may include information about restrictions placed upon such parking opportunities 204, such as neighborhood residence; loading zones; legal and illegal parking times; vehicle size or type restrictions, such as parking only for compact vehicles or electric vehicles; and permit requirements, such as handicapped zones”, [0060] “A third aspect that may vary among embodiments of the techniques presented herein involves weighting the parking probabilities 206 of various parking opportunities 204 to reflect the desirability of selecting the parking opportunity 204 as a recommendation to the user 102. While some variations of the presented techniques may only utilize the parking probabilities 206, other variations may take into account various factors that indicate whether or not the user 102 would like to choose the parking opportunity 204 if a vacancy 118 exists. In some cases, the adjustment may involve filtering the parking opportunities 204, such as excluding parking opportunities 204 that are not to be considered viable options for parking the vehicle 104. In other cases, the adjustment may involve weighting a parking probability 206 of a parking opportunity 204 to account for its desirability relative to other parking opportunities 204. For example, a device may, for respective segments 202, identify a preference score for parking the vehicle 104 in the respective parking opportunities 204 along the segment 202, and weight the parking route probabilities 212 according to the preference scores of the parking opportunities 204 of the respective segments 202 of the parking route 210.”, [0062] “Alternatively or additionally, the parking probabilities 206 of respective parking opportunities 204 may be weighted proportionally to the proximity to the destination 106, e.g., increasing the parking probability 206 for desirable parking opportunities 204 that are near the destination 106 and decreasing the parking probability 206 for desirable parking opportunities 204 that are far away from the destination 106. In some cases, the preference score may be calculated proportionally to a proximity of the respective parking opportunities 204 to the destination 106.”).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ghose in view of Della Penna to include “evaluating, by the one or more processors, the potential pullover locations in the set to determine whether any of the potential pullover locations can be categorized in a particular one of a plurality of ranked buckets, wherein a potential pullover location categorized in the particular one of the plurality of ranked buckets which is closest to the destination is selected for the log data” as disclosed in Puri. At the time the invention was filed, one of ordinary skill in the art would have been motivated to modify the invention in this way in order to associate and quantify desirable locations with a higher ranking in the evaluation of where to pullover (see at least Puri [0058] “This information may be used to … adjust the consideration of the parking opportunity 204 for the parking routes based on the circumstances of the user 102 and the vehicle 104 (e.g., a parking opportunity 206 that is reserved for electric vehicles may be included or excluded based on whether the user 102 is driving an electric vehicle 104).”).
Regarding claim 5, Ghose further teaches wherein the destination is a pickup or drop off location for a passenger or good ([0084] “In certain embodiments, the possible parking locations pertain to curbside pick-ups and/or drop-off locations for a passenger to exit from and enter the vehicle 10.”)
Regarding claim 13, Ghose does not teach but Puri teaches wherein each of the plurality of predetermined types of regions of interest is associated with a ranked bucket, and wherein selecting second the pullover location is further based on a respective ranked bucket associated with each of the plurality of predetermined types of regions of interest (see at least Puri [0058] “As a first such example, a parking probability data source 308 may include information about restrictions placed upon such parking opportunities 204, such as neighborhood residence; loading zones; legal and illegal parking times; vehicle size or type restrictions, such as parking only for compact vehicles or electric vehicles; and permit requirements, such as handicapped zones”, [0060] “A third aspect that may vary among embodiments of the techniques presented herein involves weighting the parking probabilities 206 of various parking opportunities 204 to reflect the desirability of selecting the parking opportunity 204 as a recommendation to the user 102. While some variations of the presented techniques may only utilize the parking probabilities 206, other variations may take into account various factors that indicate whether or not the user 102 would like to choose the parking opportunity 204 if a vacancy 118 exists. In some cases, the adjustment may involve filtering the parking opportunities 204, such as excluding parking opportunities 204 that are not to be considered viable options for parking the vehicle 104. In other cases, the adjustment may involve weighting a parking probability 206 of a parking opportunity 204 to account for its desirability relative to other parking opportunities 204. For example, a device may, for respective segments 202, identify a preference score for parking the vehicle 104 in the respective parking opportunities 204 along the segment 202, and weight the parking route probabilities 212 according to the preference scores of the parking opportunities 204 of the respective segments 202 of the parking route 210.”, [0062] “Alternatively or additionally, the parking probabilities 206 of respective parking opportunities 204 may be weighted proportionally to the proximity to the destination 106, e.g., increasing the parking probability 206 for desirable parking opportunities 204 that are near the destination 106 and decreasing the parking probability 206 for desirable parking opportunities 204 that are far away from the destination 106. In some cases, the preference score may be calculated proportionally to a proximity of the respective parking opportunities 204 to the destination 106.”).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ghose in view of Della Penna to include “wherein each of the plurality of predetermined types of regions of interest is associated with a ranked bucket, and wherein identifying the identified pullover location is further based on the ranked buckets associated with each of the plurality of predetermined types of regions of interest” as disclosed in Puri. At the time the invention was filed, one of ordinary skill in the art would have been motivated to modify the invention in this way in order to associate and quantify desirable locations with a higher ranking in the evaluation of where to pullover (see at least Puri [0058] “This information may be used to … adjust the consideration of the parking opportunity 204 for the parking routes based on the circumstances of the user 102 and the vehicle 104 (e.g., a parking opportunity 206 that is reserved for electric vehicles may be included or excluded based on whether the user 102 is driving an electric vehicle 104).”).
Regarding claim 14, Ghose does not teach but Puri teaches identifying, for each potential pullover location of the set of potential pullover locations, one of a plurality of ranked buckets based on any of the plurality of predetermined types of regions of interest which at least partially overlap with a respective potential pullover location of the set of potential pullover locations, and wherein selecting the second pullover location is further based on the plurality of ranked buckets (see at least Puri [0058] “As a first such example, a parking probability data source 308 may include information about restrictions placed upon such parking opportunities 204, such as neighborhood residence; loading zones; legal and illegal parking times; vehicle size or type restrictions, such as parking only for compact vehicles or electric vehicles; and permit requirements, such as handicapped zones”, [0060] “A third aspect that may vary among embodiments of the techniques presented herein involves weighting the parking probabilities 206 of various parking opportunities 204 to reflect the desirability of selecting the parking opportunity 204 as a recommendation to the user 102. While some variations of the presented techniques may only utilize the parking probabilities 206, other variations may take into account various factors that indicate whether or not the user 102 would like to choose the parking opportunity 204 if a vacancy 118 exists. In some cases, the adjustment may involve filtering the parking opportunities 204, such as excluding parking opportunities 204 that are not to be considered viable options for parking the vehicle 104. In other cases, the adjustment may involve weighting a parking probability 206 of a parking opportunity 204 to account for its desirability relative to other parking opportunities 204. For example, a device may, for respective segments 202, identify a preference score for parking the vehicle 104 in the respective parking opportunities 204 along the segment 202, and weight the parking route probabilities 212 according to the preference scores of the parking opportunities 204 of the respective segments 202 of the parking route 210.”).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ghose in view of Della Penna to include “identifying, for each given one of the potential pullover locations of the set, one of a plurality of ranked buckets based on any of the plurality of predetermined types of regions of interest which at least partially overlap with the given one, and wherein identifying the pullover location is further based on the identified ones of the plurality of ranked buckets” as disclosed in Puri. The motivation for making this modification to the invention is the same as that in the rejection of claim 13.
Regarding claim 15, Puri further teaches wherein each of the plurality of ranked buckets is associated with one or more of the plurality of predetermined types of regions of interest (see at least Puri [0060] “A third aspect that may vary among embodiments of the techniques presented herein involves weighting the parking probabilities 206 of various parking opportunities 204 to reflect the desirability of selecting the parking opportunity 204 as a recommendation to the user 102. While some variations of the presented techniques may only utilize the parking probabilities 206, other variations may take into account various factors that indicate whether or not the user 102 would like to choose the parking opportunity 204 if a vacancy 118 exists. In some cases, the adjustment may involve filtering the parking opportunities 204, such as excluding parking opportunities 204 that are not to be considered viable options for parking the vehicle 104. In other cases, the adjustment may involve weighting a parking probability 206 of a parking opportunity 204 to account for its desirability relative to other parking opportunities 204. For example, a device may, for respective segments 202, identify a preference score for parking the vehicle 104 in the respective parking opportunities 204 along the segment 202, and weight the parking route probabilities 212 according to the preference scores of the parking opportunities 204 of the respective segments 202 of the parking route 210.”).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ghose in view of Della Penna to include “wherein each of the plurality of ranked buckets is associated with one or more of the plurality of predetermined types of regions of interest” as disclosed in Puri. The motivation for making this modification to the invention is the same as that in the rejection of claim 13.
Regarding claim 18, Ghose teaches wherein evaluating the first pullover location further includes determining whether the first pullover location was a success or a failure based on a result of the comparing (see at least Ghose Fig. 7, [0093] first and second parking situations/ pullover locations shown in Fig. 7 are compared based on score and overlap with types of regions, and first pullover location is evaluated as failure in comparison to second pullover location because it has lower score and greater overlap with traffic flow and lane regions).
8. Claims 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Ghose in view Della Penna, and further in view of Herbach et al. (US 2017/0274901 A1).
Herbach is considered analogous to the claimed invention because they are in the same field of vehicle navigation and control (see MPEP 2141.01(a)).
Regarding claim 7, Ghose does not teach but Herbach teaches wherein identifying the set of potential pullover locations includes evaluating a plurality of splices of an edge of a roadway to determine whether any objects are located within an area of each given splice of the plurality of splices where the autonomous vehicle would be if parked adjacent to a portion of the edge of each given splice (see at least Herbach Figs. 3, 4A, [0078] “At block 302, the method 300 includes identifying a region of a road ahead of an autonomous vehicle in which to pull over and stop the autonomous vehicle based on lane boundaries of the road, one or more road boundaries indicating an edge of the road, and a size of the autonomous vehicle”, [0105] “The computing device may divide each region into chunks, as illustrated by the chunks 410 of region 402, for example. The computing device may first analyze region 402, although the computing device may be configured to analyze other regions prior to or after analyzing region 402. Due to the presence of another vehicle 412 currently parked in the curb lane 408D of region 402 alongside the curb 414 of the road and taking up significant space of region 402, the computing device may determine that region 402 is not suitable for pullover for at least the reason of the other vehicle 412.”).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ghose in view of Della Penna to include “wherein identifying the set of potential pullover locations includes evaluating a plurality of splices of an edge of a roadway to determine whether any objects are located within an area of each given splice of the plurality of splices where an autonomous vehicle would be if parked adjacent to a portion of the edge of each given splice” as disclosed in Herbach. At the time the invention was filed, one of ordinary skill in the art would have been motivated to modify the invention in this way in order to identify portions of the roadway that can be used as temporary pullover locations which are not designated parking spots (see at least Herbach [0084] “Further, if D_l plus the width of the autonomous vehicle (w) plus a threshold distance (T_p) is less than D_e, the computing device may determine that the given chunk is safe or otherwise suitable to pull over the autonomous vehicle at that given chunk of the respective region. In other words, this result indicates that the edge of the road is further from the rightmost lane than the width of the car plus a buffer distance (T_p). Alternatively, if D_l plus w plus T_p is greater than D_e, the computing device may determine that the given chunk is not safe or otherwise not suitable to pull over the autonomous vehicle at that given chunk of the respective region.”, [0089] “In some embodiments, the computing device may be configured to take chunks from one region that have been identified as safe chunks and combine them with safe chunks from another region. In other embodiments, instead of the computing device first identifying regions of interest and then dividing them into chunks, the computing device may be configured to identify smaller chunks (e.g., smaller regions) individually as it drives.”)
Regarding claim 8, Herbach teaches wherein the splices of the plurality of splices are shorter in length than the autonomous vehicle (see at least Herbach Fig. 4A[0083] “Namely, the computing device may take identified regions of interest from the predetermined map and divide the regions into smaller “chunks” of similar or varying length, while the width of the chunks may be the same as the width of the road (e.g., from a leftmost road boundary to a rightmost road boundary) or may be a longer width. For example, each region may be divided into chunks of 50 centimeters in length. As another example, each region may be divided into chunks of 1 meter in length.”).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ghose in view of Della Penna to include “wherein the splices of the plurality of splices are shorter in length than the autonomous vehicle” as disclosed in Herbach. The motivation for making this modification to the invention is the same as that in the rejection of claim 7.
Regarding claim 9, Herbach teaches merging adjacent splices of the plurality of splices where the autonomous vehicle would overlap with a common type of region of interest if parked adjacent to a portion of the edge of each of the merged adjacent splices (see at least Herbach [0089] “In some embodiments, the computing device may be configured to take chunks from one region that have been identified as safe chunks and combine them with safe chunks from another region. In other embodiments, instead of the computing device first identifying regions of interest and then dividing them into chunks, the computing device may be configured to identify smaller chunks (e.g., smaller regions) individually as it drives.”).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ghose in view of Della Penna to include “merging adjacent splices of the plurality of splices where an autonomous vehicle would overlap with a common type of region of interest if parked adjacent to a portion of the edge of each of the merged adjacent splices” as disclosed in Herbach. The motivation for making this modification to the invention is the same as that in the rejection of claim 7.
Regarding claim 10, Herbach teaches wherein identifying the set of potential pullover locations is based on the merged adjacent splices and at least one unmerged splice (see at least Herbach [0105] “The computing device may divide each region into chunks, as illustrated by the chunks 410 of region 402, for example. The computing device may first analyze region 402, although the computing device may be configured to analyze other regions prior to or after analyzing region 402. Due to the presence of another vehicle 412 currently parked in the curb lane 408D of region 402 alongside the curb 414 of the road and taking up significant space of region 402, the computing device may determine that region 402 is not suitable for pullover for at least the reason of the other vehicle 412. The computing device may then analyze region 404. In the example shown, the computing device may identify a red curb 416 (e.g., a “no parking zone”) as the rightmost boundary of the region 404, and may thus determine that region 404 is not suitable for pullover for at least the reason of the red curb 416.”).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ghose in view of Della Penna to include “wherein identifying the set of potential pullover locations is based on the merged adjacent splices and at least one unmerged splice” as disclosed in Herbach. The motivation for making this modification to the invention is the same as that in the rejection of claim 7.
9. Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Ghose in view of Della Penna, and further in view of and further in view of Nishiyama et al. (US 2022/0101633 A1).
Nishiyama is considered analogous to the claimed invention because they are in the same field of vehicle navigation and control (see MPEP 2141.01(a)).
Regarding claim 11, Ghose does not teach but Nishiyama teaches discarding any potential pullover locations of the set of potential pullover locations which at least partially overlap with one of the plurality of predetermined types of regions of interest identified as being unsafe (see at least Nishiyama [0031] “The map database 13a may store places where the autonomous driving vehicle 40 cannot stop. In this case, the boarding permission determination device 1 recognizes places that exclude those where vehicles cannot stop as places where the autonomous driving vehicle 40 can safely stop.”).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ghose in view of Della Penna to include “discarding any of the potential pullover locations of the set which at least partially overlap with one of a plurality of predetermined types of regions of interest identified as unsafe” as disclosed in Nishiyama. At the time the invention was filed, one of ordinary skill in the art would have been motivated to modify the invention in this way in order to not include for consideration pullover locations which are unsafe or illegal.
Regarding claim 12, Nishiyama teaches wherein the one of a plurality of predetermined types of regions of interest identified as unsafe includes railroad tracks (see at least Nishiyama [0031] “Locations where vehicles cannot stop include places where stopping is prohibited by law, such as around intersections, pedestrian crossings, railroad crossings, and the like.”).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ghose in view of Della Penna to include “wherein the one of a plurality of predetermined types of regions of interest identified as unsafe includes railroad tracks” as disclosed in Nishiyama. The motivation for making this modification to the invention is the same as that in the rejection of claim 11.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Zhang et al. (US 20190318267 A1), [0017], [0058]
Sadeghi et al. (US 2019/0324458 A1), abstract, Fig. 1, Fig. 5, [0024], [0026]
Aragon et al. (US 20190384292 A1), [0026], [0149]
Becker (US 20190378363 A1), [0136]
Walther et al. (US 10599546 B1), column 3, lines 17-22, column 4, lines 16-21
Case et al. (US 2021/0302981 A1), [0022], [0047]-[0049]
These references teach an update, adjustment, calibration, or improvement to an autonomous vehicle software as a result of an evaluation.
Conclusion
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 extension fee 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 date of this final action.
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/Shelley Chen/
Patent Examiner
Art Unit 3665
January 16, 2026
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