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 .
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claims 1-5, 7-8, 10-12, 14, 16-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Au et al., US 20100114416 A1 (herein, Au) in view of Cionca et al., US 20200217668 A1 (herein, Cionca), and in further view of Pfeiffer et al., US 10929711 B1 (herein, Pfeiffer).
Regarding Claims 1, and 14, Au discloses, a system (Abstract – system) comprising:
one or more processors (¶[0059] – “…processor…”; and
one or more non-transitory computer-readable media storing computer executable instructions that, when executed (¶[0060] – “…computer readable media may comprise, for example, non-volatile memory devices…or any other media that can be used to carry or store desired program code in the form of computer executable instructions or data structures….”), cause the one or more processors to perform operations comprising:
receiving a path associated with controlling an autonomous vehicle through an environment (FIG. 1 illustrates path and ¶[0021] – “…includes the detection of traversable areas, non-traversable areas, or obstacles…the foregoing allows the autonomous vehicle to plan a route and maneuver such…”);
receiving sensor data from a sensor associated with the autonomous vehicle (¶[0009] – “…and a geo-location unit for the autonomous vehicle, with the geo-location unit comprising one or more global positioning system sensors…”);
associating a first portion of the sensor data with a first bin (FIG. 1 and illustrates the first scan segment 120 at t0), the first bin associated with a first distance along the path (FIG. 1 illustrates the first path from vehicle 100 to t0);
determining, based at least in part on the first portion of the sensor data, a first estimated ground surface associated with the first bin (FIG. 3 and ¶[0048] – “….ground plane processing identifies the range bins that are road elements based on a history of the road elements in previous LADAR scans. The world coordinates of the road elements in previous LADAR scans, which are stored in a buffer, are used to estimate a ground plane….”);
associating a second portion of the sensor data with a second bin (FIG. 1 illustrates second portion between the second and third dotted line along segment d), the second bin
associated with a second distance along the path (FIG. 1 illustrates the first path from vehicle 100 to second and third dotted line from t0);
determining a ray between the sensor to a candidate lidar point of the second portion of the sensor data (FIG. 1 illustrates the ray 116 to the dotted lines that are the lidar points of 122);
determining whether the ray intersects the first estimated ground surface (FIG. 1 illustrates the ray intersecting the ground surface); and
one of:
determining a second ground surface associated with the second bin based on the subset of the second portion of the sensor data (FIG. 1 illustrates the second ground surface from the second to third dashed line starting at t0).
Au disclose the candidate lidar point, sensor based data, ground surface, ray and autonomous vehicle but does not disclose,
excluding the candidate lidar point from a subset of the second portion of the sensor data based at least in part on determining the ray intersects the first estimated ground surface, or
including the candidate lidar point in the subset of the second portion of the sensor data based at least in part on determining the ray does not intersect the first estimated ground surface;
controlling the autonomous vehicle based on the second ground surface.
However, Cionca teaches,
excluding the candidate lidar point from a subset of the second portion of the sensor data based at least in part on determining the ray intersects the first estimated ground surface (¶[0063] – “…above the ground surface (in order to exclude Lidar reflections from the ground surface),…”), or
including the candidate lidar point in the subset of the second portion of the sensor data based at least in part on determining the ray does not intersect the first estimated ground surface (¶[0063] – “…the broadcast object data may include a point cloud and/or object data… a reference point 155 of an object included in a list of objects…”).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the system as disclosed by Au to include the exclusion and inclusion of the corresponding subset as taught by Cionca. Doing so, allows the system to determine which subset of the ground surface permits the determination of the ground surface and thus the vehicle can travel of the surface that provides the greatest factor of safety.
Modified Au discloses the autonomous vehicle but does not disclose,
controlling the autonomous vehicle based on the second ground surface.
However, Pfeiffer teaches,
controlling the autonomous vehicle based on the second ground surface (Col. 20 and lines 20-45: “…; and controlling the autonomous vehicle based at least in part on the object associated with at least one of the dynamic grid…”).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the system as disclosed by Au to include the exclusion and inclusion of the corresponding subset as taught by Pfeiffer. Doing so, allows the system to control the autonomous vehicle based on the ground surface and this provides the capability of driving on the surface that is safer for both the vehicle and passenger.
Regarding Claims 2, 8, and 17, modified Au further discloses, wherein the ray intersects the first ground surface, the operations further comprising:
determining an intersection point where the ray intersects the first ground
surface (FIG. 1 illustrates the intersection point on the first ground surface); and
projecting the candidate lidar point to a location above the intersection point to identify a potential obstacle location (¶[0043] – “…The road segment determination program 530 outputs labels for the road, curbs, obstacles, sidewalks, and the like. A representative single LADAR range scan, shown in FIG. 6, can include segments of curbs, road, obstacles,…”).
Regarding Claim 3, modified Au further discloses, the operations further comprising:
aggregating locations of excluded candidate lidar points over time to identify a persistent reflection area (FIG. 4 and ¶[0051] – “…with the reflectance returned as an intensity value. As road markings have different reflection properties than a road surface, such properties are used to identify a road lane based on intensity values….”) and
determining a likelihood (Examiner’s Note: “likelihood” defined on ¶[0059]) of a puddle being associated with the persistent reflection area (¶[0042] – “…occurs when the surface has poor reflection, e.g., a wet surface after raining or too shallow a viewing angle…..”).
Regarding Claims 4, 10, and 19, modified Au further discloses, the operations further comprising:
detecting an object based at least in part on the second ground surface (¶[0004] – “…Objects on and off the road such as cars, cycles, and pedestrians add more complexity to the scenario….”); and
controlling the autonomous vehicle based on the object (¶[0004] – “…Autonomous land vehicles can travel at various speeds under diverse road constructs….”).
Regarding Claim 5, Au discloses, a method (Abstract – “…and method…”) comprising:
receiving sensor data from a sensor associated with a vehicle (¶[0009] – “…and a geo-location unit for the autonomous vehicle, with the geo-location unit comprising one or more global positioning system sensors…”);
obtaining a first ground surface associated with a first region along a path of the vehicle (FIG. 1 and illustrates the first scan segment 120 at t0);
determining, based on the sensor data, a plurality of candidate sensor data points associated with a second region along the path of the vehicle (FIG. 1 illustrates second portion between the second and third dotted line along segment d);
for a candidate sensor data point of the plurality of candidate sensor data points (FIG. 1 and illustrates the first candidate sensor data point 120 at t0),:
determining a line from the sensor to the candidate sensor data point (FIG. 1 illustrates the line 116 to the dotted lines that are the lidar points of 122); and
determining whether the line passes through the first ground surface (FIG. 1 illustrates the line intersecting the ground surface).
Au does not disclose,
where it is determined that the line passes through the first ground surface, excluding the candidate sensor data point from a ground plane estimation;
where it is determined that the line does not pass through the first ground surface, determining, based on the candidate sensor data point, a second ground surface associated with the second region along the path of the vehicle.
However, Cionca teaches,
where it is determined that the line passes through the first ground surface, excluding the candidate sensor data point from a ground plane estimation (¶[0063] – “…above the ground surface (in order to exclude Lidar reflections from the ground surface),…”);
where it is determined that the line does not pass through the first ground surface, determining, based on the candidate sensor data point, a second ground surface associated with the second region along the path of the vehicle (¶[0063] – “…above the ground surface (in order to exclude Lidar reflections from the ground surface),…”).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the system as disclosed by Au to include the exclusion and inclusion of the corresponding subset as taught by Cionca. Doing so, allows the system to determine which subset of the ground surface permits the determination of the ground surface and thus the vehicle can travel of the surface that provides the greatest factor of safety.
Modified Au discloses the autonomous vehicle but does not disclose,
causing control of the vehicle based at least in part on the second ground surface.
However, Pfeiffer teaches,
causing control of the vehicle based at least in part on the second ground surface (Col. 20 and lines 20-45: “…; and controlling the autonomous vehicle based at least in part on the object associated with at least one of the dynamic grid…”).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the system as disclosed by Au to include the exclusion and inclusion of the corresponding subset as taught by Pfeiffer. Doing so, allows the system to control the autonomous vehicle based on the ground surface and this provides the capability of driving on the surface that is safer for both the vehicle and passenger.
Regarding Claim 7 and 16, modified Au corresponds to Claims 1 and 5.
Regarding Claims 11 and 20, modified Au further discloses, further comprising:
receiving environmental condition data indicating whether a road surface is wet (¶[0042] – “…occurs when the surface has poor reflection, e.g., a wet surface after raining or too shallow a viewing angle…..”) or dry; and
adjusting a tolerance for determining whether the line passes through the first ground surface based on the environmental condition data (FIG. 1 illustrates path and ¶[0021] – “…includes the detection of traversable areas, non-traversable areas, or obstacles…the foregoing allows the autonomous vehicle to plan a route and maneuver such…” – i.e. a tolerance re environmental conditions).
Regarding Claim 12, modified Au further discloses, wherein the sensor data comprises at least one of:
lidar data (¶[0005] – “…LIDAR…”); and
radar data (¶[0005] – “…radar…”).
Regarding Claim 13, modified Au further discloses, further comprising:
sending the likelihood of a persistent reflection area to a computing system associated with a plurality of vehicles (¶[0021] – “… range scan and intensity scan data are processed with navigation data to provide a situation awareness,…”), such that data associated with the likelihood of a persistent reflection area is accessible to the plurality of vehicles (¶[0021] – “…obstacles include objects of a
certain size and height that a vehicle cannot traverse over, such as other vehicles, pedestrians, and the like. The reflectance measurements from the intensity scan data ….”)
Allowable Subject Matter
Claims 6, 9, 13, 15, 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS G DEL VALLE whose telephone number is (303)297-4313. The examiner can normally be reached Monday-Friday, 0730 - 1630 MST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne Antonucci can be reached at (313) 446-6519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LUIS G DEL VALLE/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666
Prosecution Insights