DETAILED ACTION
Response to Arguments
Applicant’s arguments, see application, filed 06/25/2026, with respect to the 112 rejection and DP rejection have been fully considered and are persuasive. The rejections above have been withdrawn.
Applicant’s arguments with respect to claims 1-6 and 9-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 .
Terminal Disclaimer
The terminal disclaimer filed on 06/25/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Patent # 10,621,452 & 12,067,787 have been reviewed and is accepted. The terminal disclaimer has been recorded.
Claim Rejections - 35 USC § 103
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.
Claims 1-2, 5-6, 9-12, 15-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Iagnemma (US 20180113455) in view of Nedrich et al. (herein after will be referred to as Nedrich) (Detecting behavioral zones in local and global camera views) in view of Kentley (US 20170132934) and in further view of Kojo et al. (herein after will be referred to as Kojo) (US 20160209845).
Regarding claim 1, Iagnemma discloses a computer-implemented method, comprising:
receiving real-time sensor data from one or more sensors associated with a vehicle, the real-time sensor data describing a physical environment surrounding the vehicle and including static and dynamic objects; [See Iagnemma [0005] sensors to measure properties of the vehicles surroundings. Also, see 0003 and/or 0010, real time data for an AV. Also, see 0121, objects such as traffic cones/road signs and a moving pedestrian.]
identifying candidate interaction points in the physical environment for the vehicle to stop based at least in part on historical interaction points [See Iagnemma [0007-0008] Historical environment information and/or 0021, stored data is maintained indicative of potential stopping places and the potential stopping places are identified as part of static map data for the region. Also, see 0016, analyzing current information includes analyzing distances of respective stopping places from the goal position.]
filtering the candidate interaction points to exclude interaction points that are obstructed by the static or predicted to be obstructed by the dynamic objects within a selected period of time based on the predicted future positions. [See Iagnemma [0121] Determining when an area for stopping is valid when the stopping area is occupied by other vehicles, objects such as a fallen tree or construction debris, and a moving object such as a pedestrian. Also, see 0021, stored data is maintained indicative of potential stopping places and 0021, current signals are received that represent perceptions of actual conditions at one or more of the potential stopping places. Also, see 0010, AV generates control actions based on both real-time sensor data and prior information. Therefore, this will exclude parking spots obstructed by static objects and/or dynamic objects.]
Iagnemma does not explicitly disclose
determining trajectories of the dynamic objects by tracking motion vectors of the dynamic objects over time;
predicting future positions of the dynamic objects based on the trajectories of the dynamic objects;
aggregating at least a portion of the motion vectors to identify an access feature of a physical structure, the access feature corresponding to ingress to or egress from the physical structure;
identifying candidate interaction points in the physical environment for the vehicle to stop based at least in part on
However, Nedrich does disclose
aggregating at least a portion of the motion vectors to identify an access feature of a physical structure, the access feature corresponding to ingress to or egress from the physical structure; [See Nedrich [Introduction] An important step when seeking to understand a scene is to identify regions where activity enters and exits, and may correspond to a doorway. For tasks, such as object tracking, entry regions allow for more knowledgeable tracker initialization. Also, see Abstract, These observations (i.e. tracking data) are then clustered to produce a set of potential entry and exit regions within a scene.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Iagnemma to add the teachings of Nedrich, in order to cluster object trajectories to detect entry or exit regions within a scene to improve upon monitoring traffic at these regions [See Nedrich [Introduction last paragraph]]. This will improve upon the AV parking in Iagnemma by monitoring traffic at these regions (i.e. a parking spot by a busy office door will need to be monitored more closely).
Iagnemma (modified by Nedrich) do not explicitly disclose
predicting future positions of the dynamic objects based on the trajectories of the dynamic objects;
identifying candidate interaction points in the physical environment for the vehicle to stop based at least in part on
However, Kentley does disclose
predicting future positions of the dynamic objects based on the trajectories of the dynamic objects; [See Kentley [0067] Determine future state of objects via prediction, tracking the object. Also, see 0057, predict the behavior of external objects.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Iagnemma (modified by Nedrich) to add the teachings of Kentley, in order to facilitate updated implementations for an AV which are better suited for the purpose of addressing safety risks while navigating the AV [See Kentley [0006-0007]]
Iagnemma (modified by Nedrich and Kentley) do not explicitly disclose
identifying candidate interaction points in the physical environment for the vehicle to stop based
However, Kojo does disclose
identifying candidate interaction points in the physical environment for the vehicle to stop [See Kojo [0056] Sensors to obtain information regarding the physical environment surrounding the AV. Also, see 0079, AV identifies the entrance to the building as the primary destination for the point of interest and associates parking spots with this. Also, see 0068, AV uses information from vehicle transportation networks and information identified by one or more on-vehicle sensors. Also, see 0156, distance between parking location and defined destination is within a threshold.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Iagnemma (modified by Nedrich and Kentley) to add the teachings of Kojo, in order to utilize a buildings entrance as the goal position in Iagnemma. This will improve upon AV parking such that the user is delivered to the closest distance possible to the building’s entrance.
Regarding claim 2, Iagnemma (modified by Nedrich, Kentley and Kojo) disclose
the method of claim 1. Furthermore, Iagnemma discloses
further comprising: accessing historical map data for a plurality of geographic areas, wherein the historical map data describes known physical structures and the historical interaction points. [See Iagnemma [0107] Google Earth 3D model of the environment. Also, see 0021, stored data is maintained indicative of potential stopping places.]
Regarding claim 5, Iagnemma (modified by Nedrich, Kentley and Kojo) disclose
the method of claim 1. Furthermore, Iagnemma does not explicitly disclose
wherein the identifying the candidate interaction points comprises: applying image segmentation techniques to images of physical structures based on the real-time sensor data captured by the one or more sensors to identify boundaries between the physical structures.
However, Kentley does disclose
wherein the identifying the candidate interaction points comprises: applying image segmentation techniques to images of physical structures based on the real-time sensor data captured by the one or more sensors to identify boundaries between the physical structures. [See Kentley [0109] Segment portions of image data to distinguish objects from each other (i.e. the boundaries of the objects will indeed be determined inherently such that the objects are distinguished).]
Applying the same motivation as applied in claim 1.
Regarding claim 6, Iagnemma (modified by Nedrich, Kentley and Kojo) disclose
the method of claim 1. Furthermore, Iagnemma discloses
further comprising: updating a three-dimensional interaction point map based on the filtered candidate interaction points, and [See Iagnemma [0107] 3D model of the local environment. Also, see 0021, The stored data is updated based on changes in the perceptions of actual conditions. Also, see 0021, stored data is maintained indicative of potential stopping places and 0021, current signals are received that represent perceptions of actual conditions at one or more of the potential stopping places.]
causing distribution of the updated three-dimensional interaction point map to a fleet of vehicles over one or more computer networks. [See Iagnemma [0107] 3D model of the local environment. Also, see 0021, The stored data is updated based on changes in the perceptions of actual conditions. Also, see 0022, Distributing information from one of the vehicles to other vehicles of the fleet via crowd sourcing.]
Regarding claim 9, Iagnemma (modified by Nedrich, Kentley and Kojo) disclose
the method of claim 1. Furthermore, Iagnemma does not explicitly disclose
wherein the access feature comprises a doorway for accessing the physical structure.
However, Nedrich does disclose
wherein the access feature comprises a doorway for accessing the physical structure. [See Nedrich [Introduction] An important step when seeking to understand a scene is to identify regions where activity enters and exits, and may correspond to a doorway. For tasks, such as object tracking, entry regions allow for more knowledgeable tracker initialization. Also, see Abstract, These observations (i.e. tracking data) are then clustered to produce a set of potential entry and exit regions within a scene.]
Applying the same motivation as applied in claim 1.
Regarding claim 10, Iagnemma (modified by Nedrich, Kentley and Kojo) disclose
the method of claim 1. Furthermore, Iagnemma discloses
further comprising: detecting the static objects in the physical environment based on the real-time sensor data, including at least one of a fire hydrant, a crosswalk, or a parking restriction, wherein each static obstacle is determined to be within a predetermined distance from at least one of the candidate interaction points. [See Iagnemma [0026] Stopping places with specified thresholds for acceptability, and the information includes whether the vehicle can legally stop at the potential stopping place. Also, see 0070-0071, parking restrictions.]
Regarding claim 11, see examiners rejection for claim 1 which is analogous and applicable for the rejection of claim 11.
Regarding claim 12, see examiners rejection for claim 2 which is analogous and applicable for the rejection of claim 12.
Regarding claim 15, see examiners rejection for claim 5 which is analogous and applicable for the rejection of claim 15.
Regarding claim 16, see examiners rejection for claim 1 which is analogous and applicable for the rejection of claim 16.
Regarding claim 17, see examiners rejection for claim 2 which is analogous and applicable for the rejection of claim 17.
Regarding claim 20, see examiners rejection for claim 5 which is analogous and applicable for the rejection of claim 20.
Claims 4, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Iagnemma (US 20180113455) in view of Nedrich (Detecting behavioral zones in local and global camera views) in view of Kentley (US 20170132934) in view of Kojo (US 20160209845) and in further view of Assaf et al. (herein after will be referred to as Assaf) (US 20180165518).
Regarding claim 4, Iagnemma (modified by Nedrich, Kentley and Kojo) disclose the method of claim 1. Furthermore, Iagnemma does not explicitly disclose
wherein the identifying the candidate interaction points comprises: disambiguating physical structures using LiDAR data in the real-time sensor data to distinguish between buildings based on differences in at least one of construction materials, geometric shapes, or foliage density.
However, Kentley does disclose
wherein the identifying the candidate interaction points comprises: disambiguating physical structures using LiDAR data in the real-time sensor data to distinguish between buildings [See Kentley [0073] Object detector to distinguish objects relative to other features in the environment. Also, see 0072, detection of objects using Lidar data. Also, see 0104, identify points to park AV.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Iagnemma to add the teachings of Kentley, in order to facilitate updated implementations for an autonomous vehicle which are better suited for the purpose of addressing safety risks while navigating the AV [See Kentley [0006-0007]].
Iagnemma (modified by Kentley) do not explicitly disclose
However, Assaf does disclose
[See Assaf [0030] Use Lidar data to detect the presence and shapes of objects and to distinguish objects from each other.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Iagnemma (modified by Kentley) to add the teachings of Assaf, in order to use obvious shapes of buildings to assist an object detector. This will provide improved object recognition [See Assaf [0019]].
Regarding claim 14, see examiners rejection for claim 4 which is analogous and applicable for the rejection of claim 14.
Regarding claim 19, see examiners rejection for claim 4 which is analogous and applicable for the rejection of claim 19.
Allowable Subject Matter
Claims 3, 13 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES T BOYLAN whose telephone number is (571)272-8242. The examiner can normally be reached Monday-Friday 7am-3pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JAMIE ATALA can be reached at 571-272-7384. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES T BOYLAN/Examiner, Art Unit 2486
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