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
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/23/2026 has been entered.
Response to Arguments
Applicant’s arguments, see pages 7-10, filed 02/23/2026, with respect to the rejection(s) of claim(s) 1-7, 11-15, 18, and 21-23 under 35 USC 103 have been fully considered and are persuasive in light of the amendments to the claims. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Boyko et al.
Claim Rejections 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 24 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. While paragraph [0073] of the present application discloses a threshold density and paragraph [0074] discloses that data point densities are lower farther away from the lidar sensor, the specification does not explicitly disclose that the predetermined threshold density is determined based on a distance of the lidar data points. Instead, paragraph [0074] discloses consideration of density “for smaller, local regions” as a means of addressing the point cloud density being based on distance, with no reference to an adjustable threshold density.
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.
Claims 9 and 10 are 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. Claim 9 recites “a single frame captured by the LIDAR sensor”; it is unclear whether this single frame is the same as the single frame recited in claim 1 or not. Claim 10 is dependent on claim 9, and thus indefinite for the same reasons. For the purposes of examination, it will be assumed that the single frames of claim 1 and 9 are the same.
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.
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 1-7, 9-11, 13-15, 18, and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hartman et al. (US 20190056504, previously cited) in view of Steinberg et al. (US 20190318177, previously cited) in view of Boyko et al. (US 8818609)
Claim 1.
Hartman et al. teaches:
receiving, by one or more computing devices for each of a plurality of LIDAR data points, an intensity of light generated by a LIDAR sensor of the vehicle as the light reflects off of a surface of an object and returns back to the LIDAR sensor, and a waveform for the plurality of LIDAR data points over a period of time
(Hartman – [0003]) “As is known in the art, the LIDAR system return data is typically incorporated into a cloud of points (‘point cloud data’). Point cloud data is a set of data points in a coordinate system”
(Hartman – [0003]) “LIDAR systems calculate the time that elapses from the moment the light beam is emitted to when the reflected light is received to determine distance to a target.”
(Hartman – [0024]) “The waveform 160 may include one or more returns 162. Each return 162 may have a predominant peak 164…, an intensity 165, a return width 166, and an integral 168.”
[Examiner Note: As the distance is determined based on the elapsed time, a person of ordinary skill in the art would have recognized that the waveform of Hartman is obtained over a period of time.]
Figure 1 further explains the intensity data obtained:
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Figure 1: An example waveform according to Hartman et al. (originally Hartman Figure 3)
determining, by the one or more computing devices
(Hartman – [0027]) The second return 162b is representative of light reflected off a phantom target 135… and may have multiple peaks 170, and a wide return width 166b relative to the intensity 165 of the second return 162b.”
See Fig. 1 for explanation of the various characteristics of the return 162b.
controlling, by the one or more computing devices, at least one of speed or direction of the vehicle in an autonomous driving mode based on the result
(Hartman – [0006]) “The method may further comprise classifying, by the controller, the return as from a phantom target or a non-amorphous target based on the processing, and controlling, by a machine controller, movement of the machine or the portion of the machine based on a result of the classifying.”
(Hartman – [0013]) “controlling movement of the machine 100 may include… adjusting the direction of movement, increasing speed of movement, decreasing speed of movement, stopping movement, and/or the like.”
Hartman et al. does not explicitly teach the use of a trained model; however, Steinberg et al. teaches:
receiving, by one or more computing devices for each of a plurality of LIDAR data points, an intensity of light generated by a LIDAR sensor of the vehicle as the light reflects off of a surface of an object and returns back to the LIDAR sensor, and a waveform for the plurality of LIDAR data points over a period of time
(Steinberg – [0275]) “a reflection signal spanning a first duration and having a narrow peak.”
inputting, by the one or more computing devices, the plurality of LIDAR data points and the waveform into a trained model
(Steinberg – [0252]) “the at least one processor may detect the at least one temporal distortion in the reflections signals by correlating a temporal sequence of detected reflection levels with a plurality of return-signal hypotheses using one or more neural networks trained to select a closest match (or to output a list of matches optionally including a confidence level for each match.”
determining, by the one or more computing devices using the trained model
(Steinberg – [0252]) “the at least one processor may detect the at least one temporal distortion in the reflections signals by correlating a temporal sequence of detected reflection levels with a plurality of return-signal hypotheses using one or more neural networks trained to select a closest match (or to output a list of matches optionally including a confidence level for each match.”
when
(Steinberg – [0331]) “a group of points with similar (e.g., within 2%, within 5%, etc.) confidence levels may form an outline that the processor may determine to be the boundary of an object.”
controlling, by the one or more computing devices… based on the result and the grouping
(Steinberg – [0331]) “This clustering may allow for the processor to direct more light flux towards clusters with lower confidence levels rather than towards clusters with higher confidence levels, ensuring that power is more efficiently distributed.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the LIDAR detection system of Hartman et al. to use a neural network as with the LIDAR detection system of Steinberg et al. Both Hartman et al. and Steinberg et al. operate in the same field of endeavor of detecting obstacles using a LIDAR system; thus, a person of ordinary skill in the art would have recognized that a neural network could be used by Hartman et al. in a similar fashion to that of Steinberg et al. (i.e., to classify detection results with a confidence level) with predictable results and a reasonable expectation of success. One would have been motivated to do this in order to determine whether a cluster of detections should be included in the point cloud (Steinberg – [0329]).
While Steinberg et al. teaches grouping point cloud data based on a confidence level, neither Hartman et al. nor Steinberg et al. explicitly teaches grouping point cloud data based on a point cloud density. However, Boyko et al. teaches:
when a density of LIDAR data points that correspond to the spurious object in a given portion of a single frame exceeds a predetermined threshold, grouping, by the one or more computing devices, LIDAR data points that correspond to the spurious object in a given portion of a single frame
(Boyko – Col. 14, lines 45-54) “positions of perceived objects and their corresponding boundary definitions are associated with a frame number or frame time. Thus, similarly shaped objects appearing in roughly similar locations in successive scans of the scene can be associated with one another to track objects in time. For perceived objects appearing in multiple point cloud frames (e.g., complete scans of the scanning zone), the object can be associated, for each frame on which the object appears, with a distinct bounding shape defining the dimensional extent of the perceived object.”
(Boyko – Col. 19, lines 53-60) “The record of an exhaust plume decision contains a density profile and an elevation profile, each associated with the exhaust plume decision. Current density and elevation profiles are compared with the record of exhaust plume decisions. When a current density and elevation profile is similar to past density and elevation profiles in a past exhaust plume decision, the controller 804 will use the past exhaust plume decision to make a current decision.”
[Examiner’s Note: In comparing current density profiles to past density profiles, there is a density value below which similarity is not recognized, and above which similarity is recognized. Thus, Boyko et al. teaches a threshold value.]
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the LIDAR detection system of Hartman et al. with the density-based classification of Boyko et al. Both Hartman et al. and Boyko et al. are directed towards using lidar to determine the presence of spurious objects; therefore, a person of ordinary skill in the art would have recognized that they could be combined in this fashion with predictable results. One would have been motivated to do this in order to assist in distinguishing between solid and non-solid reflective features detected by the LIDAR system (Boyko – Col. 3, lines 44-47).
Claim 2.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 1, as discussed above. Hartman et al. further teaches:
wherein the spurious object is distinguishable from a solid object and through which the vehicle is able to drive
(Hartman – [0014]) “the LIDAR system 130 is configured to determine whether the target 132 is a non-amorphous target 134 such as an object… or is a phantom target 135 such as the dust cloud 126”
Claim 3.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 1, as discussed above. Hartman et al. further teaches:
determining a [[likelihood]] of which of the plurality of LIDAR data points corresponding to the spurious object is further based on one or more heuristics define at least one characteristic of a waveform of the spurious object
(Hartman – [0024]) “The waveform 160 may include one or more returns 162. Each return 162 may have a predominant peak 164…, an intensity 165, a return width 166, and an integral 168.”
controlling the vehicle is further based on the [[comparison]]
(Hartman – [0006]) “The method may further comprise classifying, by the controller, the return as from a phantom target or a non-amorphous target based on the processing, and controlling, by a machine controller, movement of the machine or the portion of the machine based on a result of the classifying.”
(Hartman – [0013]) “controlling movement of the machine 100 may include… adjusting the direction of movement, increasing speed of movement, decreasing speed of movement, stopping movement, and/or the like.”
Hartman et al. does not explicitly teach a likelihood; however, Steinberg et al. further teaches:
determining a likelihood of one of the plurality of LIDAR data points corresponding to the spurious object is further based on one or more heuristics define at least one characteristic of a waveform of a [[spurious]] object
(Steinberg – [0219]) “The detection-quality value assigned to each out of one or more points of the PC may be indicative of a likelihood that the point cloud model corresponds to a real-world object located and the respective location identified by the PC point, and is not an erroneous detection.”
comparing the likelihood to the result, wherein controlling the vehicle is further based on the comparison
(Steinberg – [0329]) “confidence levels (also referred to as ‘detection-quality values’) associated with different detections may be used as part of the considerations of whether or not to include such detections in the point cloud”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the reasons given in discussion of claim 1.
Claim 4.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 3, as discussed above. Hartman et al. further teaches:
processing the waveform to determine a peak elongation, wherein the one or more heuristics is based on the peak elongation
(Hartman – [0024]) “The waveform 160 may include one or more returns 162. Each return 162 may have a predominant peak 164…, an intensity 165, a return width 166, and an integral 168.”
[Examiner Note: The specification does not provide an explicit definition of the phrase “peak elongation”. Therefore, it has been interpreted to refer to the peak width of the waveform.]
Claim 5.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 3, as discussed above. Hartman et al. further teaches:
processing the waveform to determine a number of peaks, wherein the one or more heuristics is based on the number of peaks
(Hartman – [0035]) “The derivative 172 may include one or more derivative peaks 174”
[Examiner Note: As the derivative is obtained from the original waveform, it is therefore waveform data.]
Claim 6.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 3, as discussed above. Hartman et al. further teaches:
processing the waveform to determine a peak width, wherein the one or more heuristics is based on the peak width
(Hartman – [0024]) “The waveform 160 may include one or more returns 162. Each return 162 may have a predominant peak 164…, an intensity 165, a return width 166, and an integral 168.”
Claim 7.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 3, as discussed above. Hartman et al. further teaches:
processing the waveform to determine a peak elongation relative to an expected peak width, wherein the one or more heuristics is based on the peak elongation relative to the expected peak width
(Hartman – [0024]) “The waveform 160 may include one or more returns 162. Each return 162 may have a predominant peak 164…, an intensity 165, a return width 166, and an integral 168.”
[Examiner Note: As discussed above in claim 4, “elongation” and “width” are interpreted to be synonymous.]
Claim 9.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 1, as discussed above. However, Hartman et al. does not explicitly teach a frame corresponding to the plurality of LIDAR data points. Zhu 618 teaches:
wherein the plurality of LIDAR data points corresponds to a single frame captured by the LIDAR sensor
(Boyko – Col. 14, lines 45-54) “positions of perceived objects and their corresponding boundary definitions are associated with a frame number or frame time. Thus, similarly shaped objects appearing in roughly similar locations in successive scans of the scene can be associated with one another to track objects in time. For perceived objects appearing in multiple point cloud frames (e.g., complete scans of the scanning zone), the object can be associated, for each frame on which the object appears, with a distinct bounding shape defining the dimensional extent of the perceived object.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the reasons given in discussion of claim 1.
Claim 10.
The combination of Hartman et al., Steinberg et al., Boyko et al., and Zhu 618 teaches all the limitations of claim 9, as discussed above. However, Hartman et al. does not explicitly teach a frame corresponding to a 360-degree rotation. Zhu 618 teaches:
wherein the single frame corresponds to one 360 degree rotation of the LIDAR sensor
(Boyko – Col. 21, lines 10-11) “the azimuth range of the scanning zone can be approximately 360 degrees”
It would have been obvious to combine these teachings for the reasons given in discussion of claim 1.
Claim 11.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 1, as discussed above. Hartman et al. further teaches:
wherein a type of the spurious object is vehicle exhaust or dust
(Hartman – [0045]) “a passenger in an autonomously controlled car may see on a display 156 of a user interface 146 a notification that a dust cloud 126 (or fog, or rain, or smoke, or falling snow) has been detected.”
[Examiner Note: Someone possessing ordinary skill in the art would recognize that vehicle exhaust, as the byproducts of fuel combustion, would be included in the category of “smoke”.]
Claim 13.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 1, as discussed above. Hartman et al. further teaches:
wherein a type of the spurious object is fog
(Hartman – [0045]) “a passenger in an autonomously controlled car may see on a display 156 of a user interface 146 a notification that a dust cloud 126 (or fog, or rain, or smoke, or falling snow) has been detected.”
Claim 14.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 1, as discussed above. Hartman et al. further teaches:
wherein a type of the spurious object is rain
(Hartman – [0045]) “a passenger in an autonomously controlled car may see on a display 156 of a user interface 146 a notification that a dust cloud 126 (or fog, or rain, or smoke, or falling snow) has been detected.”
Claim 15.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 1, as discussed above. Hartman et al. further teaches:
wherein a type of the spurious object is snow
(Hartman – [0045]) “a passenger in an autonomously controlled car may see on a display 156 of a user interface 146 a notification that a dust cloud 126 (or fog, or rain, or smoke, or falling snow) has been detected.”
Claim 18.
Rejected by the same rationale as claim 1.
Claim 21.
Rejected by the same rationale as claim 2.
Claim 22.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 1, as discussed above. With respect to Fig. 1 above, Hartman et al. further teaches:
wherein the waveform includes a curve fit to the plurality of LIDAR data points, wherein each of the plurality of LIDAR data points represents an intensity value
(Hartman – [0024]) “The waveform 160 may include one or more returns 162. Each return 162 may have a predominant peak 164…, an intensity 165, a return width 166, and an integral 168.”
[Examiner’s Note: As seen in Fig. 1, the waveform return provides data points representing intensities.]
Claim 23.
Hartman et al. teaches:
A vehicle
(Hartman – [0013]) “The exemplary machine 100 may be a vehicle”
a LIDAR sensor
(Hartman – [0013]) “The machine 100 (haul truck 102) further includes a LIDAR system 130 mounted on the machine 100”
(Hartman – [0015]) “The LIDAR system 130 comprises an emission and detection module 136, and a controller 138.”
a processor
(Hartman – [0013]) “The machine 100 (haul truck 102) further includes a LIDAR system 130 mounted on the machine 100”
(Hartman – [0015]) “The LIDAR system 130 comprises an emission and detection module 136, and a controller 138.”
a storage including a non-transitory computer readable medium having instructions stored thereon
(Hartman – [0018]) “Such instructions that are capable of being executed by a computer may be read into or embodied on a computer readable medium, such as the memory component 154 or provided external to the processor 152.”
The rest of the claim is rejected by the same rationale as claim 1.
Claims 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hartman et al., Steinberg et al., and Boyko et al. as applied to claims 1-8, 11-15, and 18 above, and further in view of Zhu et al. (US 8983705, previously cited).
Claim 16.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 1, as discussed above. However, Hartman et al. does not explicitly teach slowing down in dust or fog. Zhu et al. teaches:
wherein, when the type of the spurious object is dust or fog, the controlling the vehicle includes slowing down the vehicle
(Zhu – Col. 3, lines 30-35) “Example actions may include providing instructions to indicate a request to transition to a manual mode, or if remaining in autonomous mode then switching to a mode specific to driving in fogs (i.e., driving at slower speeds, allowing for larger distances to accomplish braking, turn on fog lights, etc.).”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the combination of Hartman et al. and Steinberg et al. with the vehicle fog control of Zhu 705 such that the commands of the latter are issued by the former when fog is detected. One would be motivated to do this because autonomous vehicles do not drive as well in foggy conditions (Zhu – Col. 3, lines 1-5).
Claim 19.
Rejected by the same rationale as claim 16.
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hartman et al., Steinberg et al., and Boyko et al. as applied to claim 1 above, and further in view of Chattopadhyay et al. (US 20190051006).
Claim 24.
The combination of Hartman et al., Steinberg et al., and Boyko et al. teaches all the limitations of claim 1, as discussed above. Boyko et al. teaches characterization of a point map density at a plurality of distances (Boyko – Abstract); however, Boyko et al. does not explicitly teach a variable point density threshold based on a distance. Chattopadhyay et al. teaches:
wherein the predetermined threshold of the amount of the LIDAR data points that correspond to the spurious object in the given portion of the single frame is determined based on a distance of the LIDAR data points from the LIDAR sensor
(Chattopadhyay – [0026]) “Some embodiments adjust the segmentation algorithm parameters per candidate segment based primarily on the measured range to each candidate segment.”
(Chattopadhyay – [0054]) “in the case of a density-based spatial clustering of applications with noise (DBSCAN) algorithm being used as the segmentation algorithm, the variable parameters may be the minimum number of points (typically represented as k, or MinPts) that should be present within a neighborhood of points”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the density threshold of Boyko et al. with the distance-based threshold of Chattopadhyay et al. One would have been motivated to do this in order to mitigate the issues caused by uneven distribution of data points in segmentation of the data (Chattopadhyay – [0024]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARAH A MUELLER whose telephone number is (703)756-4722. The examiner can normally be reached M-Th 7:30-12:00, 1:00-5:30; F 8:00-12:00.
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/S.A.M./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
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