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 .
Information Disclosure Statement
The Information Disclosure Statements submitted on 1/09/2026 and 2/28/2026 are in compliance with the provisions of 37 CFR 1.97 and 1.98 and have been considered.
Response to Arguments
Applicant’s arguments, see page 12 of the remarks, filed 12/18/2025, with respect to the rejections made under 35 U.S.C. 102 have been fully considered and are persuasive. As previously agreed upon in the interview conducted on 12/16/2025, the claim rejections made under 35 U.S.C. 102 have been withdrawn.
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 1-3, 7-9, 15, 16, and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Arita (US 20030218919 A1), in view of Ding (US 20190339386 A1).
Regarding Claim 1: Arita discloses a light detection and ranging system configured for far distance road surface detection ([0004] and Fig. 1A, laser radar 1 can be mounted on a car for road detection), comprising:
one or more processors (Fig. 1A, control circuit 17);
memory (Fig. 1A, memory 18); and
one or more programs stored in the memory, the one or more program instructions for ([0039] the control circuit has/is connected to a memory that stores the program that carries out the detection method):
obtaining lidar detection data samples, the lidar detection data samples being associated with signal intensities below a threshold used for near distance road surface detection ([0033] and Figs. 2B-2D, there is a maximum at time Tb but it is not detectable because the light intensity is below the threshold);
determining, based on the maximum signal intensity, whether the lidar detection data samples correspond to a far distance road surface detection ([0033-0034] the integrated data determines if there is a target or not. If the threshold value is not exceeded when the number of standard areas have been cumulatively added, then there is no target. If the cumulatively integrated data has a signal that exceeds the threshold, as shown in Fig. 2E, then there is determined to be a target).
Arita does not disclose determining, based on a sliding time window, a maximum signal intensity associated with the lidar detection data samples, or, in accordance with a determination that the lidar detection data samples correspond to a far distance road surface detection, providing far distance road surface detection data for controlling movement of a vehicle.
Ding teaches determining, based on a sliding time window, a maximum signal intensity associated with the lidar detection data samples ([0045] sliding window 606 scans across all the time samples and determines the location and validity of peaks); and
in accordance with a determination that the lidar detection data samples correspond to a far distance road surface detection, providing far distance road surface detection data for controlling movement of a vehicle ([0025] information about possible targets or obstacles can be given to an automated driving control system).
It would have been obvious to a person having ordinary skill in the art of lidar technologies before the effective filing date of the claimed invention to replace the peak determination method disclosed by Arita with the peak determination method taught by Ding. Rather than determining the peak by cumulatively adding subsequent measurements of a combined area (the combined areas are illustrated in Fig. 1B), the peak determination would be determined by the sliding time window that searches through each measurement as taught by Ding. This would be applying the known technique of a sliding time window to a known lidar system ready for improvement to yield predictable results (See MPEP 2141.III KSR Rationale D). Furthermore, it would also be obvious to further modify the lidar mounted on an automobile disclosed by Arita, by providing the distance information obtained by the lidar system to an automated driving control system as taught by Ding. Using data obtained by a lidar system mounted on a vehicle in order to provide information for autonomous control of the same vehicle would also be applying a known technique to a known device ready for improvement to yield predictable results (See MPEP 2141.III KSR Rationale D).
Regarding Claim 2: Arita, in view of Ding, teaches the system of claim 1. Arita further discloses further comprising:
a transmitter facilitating to transmit one or more light pulses to a field of view (Fig. 1A, laser diode 12 and scanner 11); and
a receiver configured to receive a return light pulse corresponding to a current transmitted light pulse (Fig. 1A, photodiode 15 and light receiver 16).
Regarding Claim 3: Arita, in view of Ding, teaches the system of claim 1. Arita further discloses wherein the one or more programs comprise further instructions for: determining whether far-distance road surface detection should be used ([0034] if a single standard area (see Fig. 1B illustrating standard vs combined areas) has no signal exceeding a threshold value, and then the adjacent standard area does have a signal above the threshold, then the far distance road surface detection is stopped and it is concluded that 1, there is no target in the first standard area, and 2, there is a target in the second standard area).
In this combination of Arita and Ding, which identifies peaks through a sliding window instead of a cumulative addition of detections, Ding teaches: if far-distance road surface detection should be used, using the far-distance road surface detection from a starting time position to an ending time position, wherein the starting time position and the ending time position are within a time interval between time positions associated with two consecutively transmitted light pulses ([0045] and Fig. 6, the sliding window for peak identification is applied over the entire detection frame). Since this is being applied for each detection frame, rather than a cumulation of detection frames, the sliding window integration occurs over a time that is between two consecutively emitted measurement signals.
Regarding Claim 7: Arita, in view of Ding, teaches the system of claim 1. In this combination, Ding further teaches: wherein determining, based on the sliding time window, the maximum signal intensity associated with the LiDAR detection data samples comprises: selecting a time width of the sliding time window (Fig. 6, sliding window 606 has a particular width smaller than the duration of the frame); and iteratively integrating, based on a starting time position and an ending time position, a plurality of subsets of the LiDAR detection data samples having corresponding time positions within the sliding time window ([0045] “Window 606 slides across in time, or, equivalently, across sequential samples, as illustrated in FIG. 6. Sliding window 606 can slide, for example, from sample number n=0 to sample number n=5,000”).
Regarding Claim 8: Arita, in view of Ding, teaches the system of claim 7. In this combination, Ding further teaches: wherein iteratively integrating the plurality of subsets of the LiDAR detection data samples having corresponding time positions within the selected time width of the sliding time window comprises: integrating a first subset of the LiDAR detection data samples having corresponding time positions within the time width of the sliding time window, the sliding time window being at the starting time position ([0045] the sliding time window can start integrating from sample number 0); and
iteratively performing: moving the sliding time window to a next time position, determining whether the next time position causes the sliding time window to exceed the ending time position, if the next time position does not cause the sliding time window to exceed the ending time position, integrating a next subset of the LiDAR detection data samples having corresponding time positions within the sliding time window at the next time position ([0045] the sliding window slides up until sample number 5000, the last sample number in Fig. 6. The integration process happens across the entire detection frame as the sliding window is iteratively moved to the next group of samples).
Regarding Claim 9: Arita, in view of Ding, teaches the system of claim 7. In this combination, Ding further teaches: wherein determining, based on the sliding time window, the maximum signal intensity associated with the LiDAR detection data samples further comprises: determining the maximum signal intensity based on results of the iterative integration, from the starting time position to the ending time position, of the plurality of subsets of the LiDAR detection data samples having corresponding time positions within the time width of the sliding time window (Fig. 9, if the peak value is larger than the previous peak, in step 918, then the previous peak is replaced by the new peak in step 20; [0045] the sliding time window iteratively integrates across all the samples from n=0 to n=5000, and the determination of a valid peak and its location is carried out by the process in the flow diagram of Fig. 9).
Regarding Claim 15: Arita, in view of Ding, teaches the system of claim 1. Arita further discloses wherein the one or more programs include further instructions for: in accordance with a determination that the LiDAR detection data samples correspond to a far-distance road surface detection, determining a time position of a return light pulse corresponding to a detected far-distance road surface ([0033] upon identifying the presence of a valid peak, the time position, at Tb for Figs. 2B-E, is identified as the time of the return pulse). In this combination, where the sliding time window taught by Ding is used to determine the presence of a pulse, instead of a cumulative addition of detection frames, the system, by identifying the peak location, identifies the time at which the light pulse has returned. Ding teaches, in Fig. 9 and paragraph [0051] that the N samples, from n=0 to n=N, are processed to identify the location of a valid peak. As seen in Fig. 6, the sample number n corresponds to time.
Regarding Claim 16: Arita, in view of Ding, teaches the system of claim 1. In this combination, where the sliding time window taught by Ding is used to determine the presence of a return pulse, Ding further teaches: wherein determining the time position of the return light pulse corresponding to the detected far-distance road surface comprises: computing the time position of the return light pulse corresponding to the detected far-distance road surface based on a weight center of the LiDAR detection data samples within the sliding time window associated with the maximum signal intensity ([0045] if a peak is in the middle of the window 606, it is compared to all the other peaks within the window at that instant. If not, it proceeds to the next sample index).
Regarding Claim 23: Arita discloses a method for performing far distance road surface detection using a LiDAR scanning system ([0004] and Fig. 1A, laser radar 1 can be mounted on a car for road detection, and scanner 11), comprising:
obtaining lidar detection data samples, the lidar detection data samples being associated with signal intensities below a threshold used for near distance road surface detection ([0033] and Figs. 2B-2D, there is a maximum at time Tb but it is not detectable because the light intensity is below the threshold);
determining, based on the maximum signal intensity, whether the lidar detection data samples correspond to a far distance road surface detection ([0033-0034] the integrated data determines if there is a target or not. If the threshold value is not exceeded when the number of standard areas have been cumulatively added, then there is no target. If the cumulatively integrated data has a signal that exceeds the threshold, as shown in Fig. 2E, then there is determined to be a target).
Arita does not disclose determining, based on a sliding time window, a maximum signal intensity associated with the lidar detection data samples, or, in accordance with a determination that the lidar detection data samples correspond to a far distance road surface detection, providing far distance road surface detection data for controlling movement of a vehicle.
Ding teaches determining, based on a sliding time window, a maximum signal intensity associated with the lidar detection data samples ([0045] sliding window 606 scans across all the time samples and determines the location and validity of peaks); and
in accordance with a determination that the lidar detection data samples correspond to a far distance road surface detection, providing far distance road surface detection data for controlling movement of a vehicle ([0025] information about possible targets or obstacles can be given to an automated driving control system).
It would have been obvious to a person having ordinary skill in the art of lidar technologies before the effective filing date of the claimed invention to replace the peak determination method disclosed by Arita with the peak determination method taught by Ding. Rather than determining the peak by cumulatively adding subsequent measurements of a combined area (the combined areas are illustrated in Fig. 1B), the peak determination would be determined by the sliding time window that searches through each measurement as taught by Ding. This would be applying the known technique of a sliding time window to a known lidar system ready for improvement to yield predictable results (See MPEP 2141.III KSR Rationale D). Furthermore, it would also be obvious to further modify the lidar mounted on an automobile disclosed by Arita, by providing the distance information obtained by the lidar system to an automated driving control system as taught by Ding. Using data obtained by a lidar system mounted on a vehicle in order to provide information for autonomous control of the same vehicle would also be applying a known technique to a known device ready for improvement to yield predictable results (See MPEP 2141.III KSR Rationale D).
Regarding Claim 24: Arita discloses a non-transitory computer readable medium storing one or more programs ([0039] there is a microcomputer with a CPU, ROM, and RAM, which use a program to carry out a method for distance measurement), the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the device to perform (Fig. 1A and [0039], the controller 17 contains this microcomputer and controls the scanner and the driver and receives data from the light receiver, and is operatively coupled with the memory 18)
obtaining lidar detection data samples, the lidar detection data samples being associated with signal intensities below a threshold used for near distance road surface detection ([0033] and Figs. 2B-2D, there is a maximum at time Tb but it is not detectable because the light intensity is below the threshold);
determining, based on the maximum signal intensity, whether the lidar detection data samples correspond to a far distance road surface detection ([0033-0034] the integrated data determines if there is a target or not. If the threshold value is not exceeded when the number of standard areas have been cumulatively added, then there is no target. If the cumulatively integrated data has a signal that exceeds the threshold, as shown in Fig. 2E, then there is determined to be a target).
Arita does not disclose determining, based on a sliding time window, a maximum signal intensity associated with the lidar detection data samples, or, in accordance with a determination that the lidar detection data samples correspond to a far distance road surface detection, providing far distance road surface detection data for controlling movement of a vehicle.
Ding teaches determining, based on a sliding time window, a maximum signal intensity associated with the lidar detection data samples ([0045] sliding window 606 scans across all the time samples and determines the location and validity of peaks); and
in accordance with a determination that the lidar detection data samples correspond to a far distance road surface detection, providing far distance road surface detection data for controlling movement of a vehicle ([0025] information about possible targets or obstacles can be given to an automated driving control system).
It would have been obvious to a person having ordinary skill in the art of lidar technologies before the effective filing date of the claimed invention to replace the peak determination method disclosed by Arita with the peak determination method taught by Ding. Rather than determining the peak by cumulatively adding subsequent measurements of a combined area (the combined areas are illustrated in Fig. 1B), the peak determination would be determined by the sliding time window that searches through each measurement as taught by Ding. This would be applying the known technique of a sliding time window to a known lidar system ready for improvement to yield predictable results (See MPEP 2141.III KSR Rationale D). Furthermore, it would also be obvious to further modify the lidar mounted on an automobile disclosed by Arita, by providing the distance information obtained by the lidar system to an automated driving control system as taught by Ding. Using data obtained by a lidar system mounted on a vehicle in order to provide information for autonomous control of the same vehicle would also be applying a known technique to a known device ready for improvement to yield predictable results (See MPEP 2141.III KSR Rationale D).
Regarding Claim 25: Arita discloses a motor vehicle comprising a lidar system configured for far distance road surface detection ([0004] and Fig. 1A, laser radar 1 can be mounted on a car for road detection), the system comprising:
one or more processors (Fig. 1A, control circuit 17);
memory (Fig. 1A, memory 18); and
one or more programs stored in the memory, the one or more program instructions for ([0039] the control circuit has/is connected to a memory that stores the program that carries out the detection method):
obtaining lidar detection data samples, the lidar detection data samples being associated with signal intensities below a threshold used for near distance road surface detection ([0033] and Figs. 2B-2D, there is a maximum at time Tb but it is not detectable because the light intensity is below the threshold);
determining, based on the maximum signal intensity, whether the lidar detection data samples correspond to a far distance road surface detection ([0033-0034] the integrated data determines if there is a target or not. If the threshold value is not exceeded when the number of standard areas have been cumulatively added, then there is no target. If the cumulatively integrated data has a signal that exceeds the threshold, as shown in Fig. 2E, then there is determined to be a target).
Arita does not disclose determining, based on a sliding time window, a maximum signal intensity associated with the lidar detection data samples, or, in accordance with a determination that the lidar detection data samples correspond to a far distance road surface detection, providing far distance road surface detection data for controlling movement of a vehicle.
Ding teaches determining, based on a sliding time window, a maximum signal intensity associated with the lidar detection data samples ([0045] sliding window 606 scans across all the time samples and determines the location and validity of peaks); and
in accordance with a determination that the lidar detection data samples correspond to a far distance road surface detection, providing far distance road surface detection data for controlling movement of a vehicle ([0025] information about possible targets or obstacles can be given to an automated driving control system).
It would have been obvious to a person having ordinary skill in the art of lidar technologies before the effective filing date of the claimed invention to replace the peak determination method disclosed by Arita with the peak determination method taught by Ding. Rather than determining the peak by cumulatively adding subsequent measurements of a combined area (the combined areas are illustrated in Fig. 1B), the peak determination would be determined by the sliding time window that searches through each measurement as taught by Ding. This would be applying the known technique of a sliding time window to a known lidar system ready for improvement to yield predictable results (See MPEP 2141.III KSR Rationale D). Furthermore, it would also be obvious to further modify the lidar mounted on an automobile disclosed by Arita, by providing the distance information obtained by the lidar system to an automated driving control system as taught by Ding. Using data obtained by a lidar system mounted on a vehicle in order to provide information for autonomous control of the same vehicle would also be applying a known technique to a known device ready for improvement to yield predictable results (See MPEP 2141.III KSR Rationale D).
Claims 4, 6, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Arita (US 20030218919 A1), in view of Ding (US 20190339386 A1), further in view of Yellepeddi (US 20200256999 A1).
Regarding Claim 4: Arita, in view of Ding, teaches the system of claim 1. However, they do not expressly teach: wherein the one or more programs comprise further instructions for enable the far distance road surface detection based on a first threshold distance.
Yellepeddi teaches: wherein the one or more programs comprise further instructions for enable the far distance road surface detection based on a first threshold distance ([0039] and Fig. 9; while Fig. 9 illustrates a
1
d
i
s
t
a
n
c
e
2
relationship, [0039] describes that this is merely an example, and “the distance-dependent threshold can decrease as a piece-wise constant function,” which means that the distance at which the threshold changes for the first time would be a first threshold distance).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the far distance road surface detection taught by Arita and Ding, such that there is a threshold distance at which the far distance road surface detection is enabled, as taught by Yellepeddi. This is beneficial because using a far distance road surface detection mode only after a threshold distance can redistribute ‘detection power’ so signal detection ability can be optimized while a false alarm detection rate is still preserved over varying distances (Yellepeddi, [0039]).
Regarding Claim 6: Arita, in view of Ding, teaches the system of claim 1. However, they do not expressly teach: further comprising one or more analog to digital converters configured to sample a return signal corresponding to a current transmitted light pulse within a starting time position and an ending time position to obtain the LiDAR detection data samples.
Yellepeddi teaches sampling return signals with an ADC ([0048] “an analog-to-digital converter (ADC) circuit coupled to the output of the amplifier”; Fig. 15, digitized signal has been output by ADC).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the receiver disclosed by Arita, such that an ADC is used to sample the return signal as taught by Yellepeddi. This would be combining the prior art elements of a receiver circuit and an ADC circuit according to known methods to yield the predictable result of determining the time at which the pulse has returned (See MPEP 2141.III KSR Rationale A).
Regarding Claim 10: Arita, in view of Ding, teaches the system of claim 1. However, they do not expressly teach: wherein determining, based on the maximum signal intensity, whether the LiDAR detection data samples correspond to the far distance road surface detection comprises: determining whether the maximum signal intensity is greater than a first intensity threshold and if the maximum signal intensity is less than or equal to the first intensity threshold, determining that the LiDAR detection samples do not correspond to a far distance road surface detection.
Yellepeddi teaches wherein determining, based on the maximum signal intensity, whether the LiDAR detection data samples correspond to the far distance road surface detection comprises: determining whether the maximum signal intensity is greater than a first intensity threshold and if the maximum signal intensity is less than or equal to the first intensity threshold, determining that the LiDAR detection samples do not correspond to a far distance road surface detection ([0039] and Fig. 9 bottom left portion. The distance dependent threshold can decrease in a piece wise function, and the distance dependent threshold, illustrated by the dashed line, decreases with distance. The lower intensity threshold, for far distances, is a first intensity threshold. If no signal exceeds this first, lower, threshold, then there is no return signal).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further modify the intensity thresholds disclosed by Arita, such that there is more than one intensity threshold and the intensity thresholds are distance dependent, as taught by Yellepeddi. This would be beneficial because signals decay over time, as illustrated by Yellepeddi in Fig. 3 and with paragraph [0025]. The farther the object, the less power/intensity the returned pulse has. By having a distance dependent threshold, the detection ability over varying distances is improved while maintaining a desired false alarm rate (Yellepeddi, [0039]).
Regarding Claim 11: Arita, in view of Ding and Yellepeddi, teaches the system of claim 10. Ding further teaches wherein the one or more programs include further instructions for: determining the first intensity threshold based on a time width of the sliding window and a sample noise floor ([0039] if the envelope of a signal is greater than a threshold, which is set determined by a certain noise level, then it is determined to be valid; [0056] the threshold for detecting a dominant peak is also set to be 0.25 times the height of the dominant peak that is within one half window width on either side of the detected peak). In the combination of claim 10, the “first intensity threshold” taught by Yellepeddi is a threshold that differentiates a weak signal from noise.
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify the signal identification in the system taught by Arita, Ding, and Yellepeddi, such that the first threshold takes into account a noise floor and window size, as further taught by Ding. This method for peak identification and validation not only identifies whether a signal has been returned, but it also identifies a dominant peak. This minimum threshold can also ignore secondary peaks that are side lobes from the dominant peak (Ding, [0056]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Arita (US 20030218919 A1), in view of Ding (US 20190339386 A1), further in view of Pacala ‘387 (US 20220291387 A1). Arita, in view of Ding, teaches the system of claim 1. They do not teach wherein the one or more programs comprise further instructions for disabling the far distance road surface detection based on a maximum detectable distance of the lidar system.
Pacala ‘387 teaches this limitation in paragraph [0086] where “the time between the emissions of pulse trains determines the maximum detectable range.” This means, after a first pulse train is emitted and the time corresponding to a maximum detectable range has passed, a second pulse train is emitted and this is the start of a new detection period.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further modify the system taught by Arita and Ding, to incorporate the step of stopping the detecting based on a maximum detectable distance of the lidar system, as taught by Pacala ‘387. This is beneficial because reflections detected after the time associated with a maximum detectable range of the lidar system are much more likely to be reflections from a subsequent emitted pulse, and having correct emission and detection times of the pulses are imperative for measuring distance by time of flight (Pacala ‘387, [0086]).
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Arita (US 20030218919 A1), in view of Ding (US 20190339386 A1), further in view of Yellepeddi (US 20200256999 A1), further in view of Zednik (DE 102017112784 A1).
Regarding Claim 12: Arita, in view of Ding and Yellepeddi, teaches the system of claim 10. In this combination, Ding further teaches wherein determining based on the maximum signal intensity, whether the LiDAR detection data samples correspond to a far distance road surface detection further comprises: if the maximum signal intensity is greater than the first intensity threshold determining whether there are additional LiDAR detection data samples corresponding to return signals (Fig. 9 and [0045] even though one peak is identified, the sliding window still scans through the rest of the samples to determine if there are other valid peaks. If another valid peak is larger than a previous peak, the previous peak is replaced with the new one).
They do not expressly teach determining whether there are additional Lidar detection data samples corresponding to return signals having signal intensities above a second intensity threshold, wherein the additional LiDAR detection data samples and the LiDAR detection data samples are both obtained based on return signals corresponding to same two consecutively transmitted light pulses.
Zednik teaches determining whether there are additional Lidar detection data samples corresponding to return signals having signal intensities above a second intensity threshold (Fig. 2 and [0034] – [0036] two detected pulses at P1 and P2, both having intensities above the threshold for noise. There is an additional intensity threshold Is and P1 has intensity I1 at a close distance, and P2 is further and has intensity I2, which is below the threshold Is.), wherein the additional LiDAR detection data samples and the LiDAR detection data samples are both obtained based on return signals corresponding to same two consecutively transmitted light pulses (Figs. 2-4, P1 and P2 are two different peaks and result in two different distances D and D’. As seen in Figs. 3 and 4, D and D’ are in different locations, and a pulse that is determined to have distance D is adjacent to a pulse determined to have a distance D’).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement the second intensity threshold taught by Zednik, into the determination of whether the received signal represents a far distance road detection taught by Arita, Ding, and Yellepeddi. This would be beneficial because additional intensity thresholds can also be used to identify which points can be clustered together to represent objects in the environment (Zednik, [0036-0038] and Figs. 3-5).
Regarding Claim 13: Arita, in view of Ding, Yellepeddi, and Zednik, teaches the system of claim 12. In this combination, Arita discloses wherein determining, based on the maximum signal intensity, whether the LiDAR detection data samples correspond to a far-distance road surface detection further comprises:
if there are no additional LiDAR detection data samples associated with return signals having signal intensities above the second intensity threshold, determining that the LiDAR detection data samples correspond to a far-distance road surface detection (Fig. 1B for illustrative purposes and [0034] If a signal in standard area 1 has no signal exceeding the threshold, and standard area 2 does, then it is determined that there is no far distance road surface detection. However, if there is not an additional Lidar detection data sample that is above a threshold, then the far distance road surface detection is continued).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Arita (US 20030218919 A1), in view of Ding (US 20190339386 A1), further in view of Yellepeddi (US 20200256999 A1), further in view of Zednik (DE 102017112784 A1), and further in view of Chung (US 20210018596 A1). Arita, in view of Ding, Yellepeddi, and Zednik, teaches the system of claim 12. They do not teach wherein the one or more programs include further instructions for: determining the second intensity threshold based on a minimum intensity of known object pulses and a multiplier.
However, Chung teaches determining the second intensity threshold based on a minimum intensity of known object pulses and a multiplier ([0033] “, the second threshold intensity is calculated using the following equation: I2=A2*(Dmax−D)+C, wherein I2 is the second threshold intensity, A2 is a second coefficient, Dmax is the first threshold distance, D is the object distance, and C is a second preset value”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the second intensity threshold in the system taught by Arita, Ding, Yellepeddi, and Zednik, such that the second intensity threshold is calculated busing a multiplier and intensity of known object pulses as taught by Chung. By taking into account the minimum intensity for which it is known that the detected object is an obstruction, the system can “determine whether the detected object is an interference, such as a raindrop, dust, snowflake and the like,” or if it is an object that will have an effect on the vehicle (Chung [0025]).
Claims 17, 18, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Arita (US 20030218919 A1), in view of Ding (US 20190339386 A1), further in view of Herman (US 20210009137 A1).
Regarding Claim 17: Arita, in view of Ding, teaches the system of claim 1. Arita further discloses wherein the one or more programs include further instructions for: causing at least a part of a perception of an environment associated with the vehicle to be generated based on the far distance road surface detection ([0043] and Fig. 3, based on the detection, distance to the target is determined. Distance to a target is a perception of an environment. [0045] describes a scenario in which it is determined that there is a vehicle in the environment).
They do not explicitly teach causing a vehicle control system to actuate a vehicle control mechanism based on the perception of the environment associated with the vehicle.
Herman teaches the determination of a perception of an environment of a vehicle (Fig. 1 and [0031] a 3D map of the area 160 in front of a vehicle 100 is created and determines information like reflectivity, locations of surfaces, and the type of surface (vegetation, asphalt, concrete, metal, painted road surface etc.)) and causing a vehicle control system to actuate a vehicle control mechanism based on the perception of the environment associated with the vehicle (Fig. 9, step 940 and [0072] if the road surface is wet, the computer may control the vehicle to slow down).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the system taught by Arita and Ding, to incorporate the vehicle navigation based on environmental conditions and determination of road surface conditions, as taught by Herman. This would be beneficial because it would enable the vehicle to be operated autonomously or semi-autonomously (Herman, [0025]). This would also enable the vehicle to be autonomously controlled in response to different road surface conditions (Herman, [0029] – [0031]).
Regarding Claim 18: Arita, in view of Ding and Herman, teaches the system of claim 17. In this combination, Herman teaches wherein the perception of the environment comprises at least one of a road shape perception or a road surface condition perception (Fig. 1 and [0054] “In one example, the computer 110 may be programmed to determine whether a surface 150 is wet at, e.g., a point 170 with location coordinates (x, y, z) based on a reflectivity R of the surface 150”).
Regarding Claim 20: Arita, in view of Ding and Herman, teaches the system of claim 18. In this combination, Herman further teaches wherein the road surface condition perception comprises a perception of at least one of: a dry road surface, a wet road surface, a flooded road surface, an icy road surface, an oily road surface, an obstructed road surface, and a changing of a road surface condition ([0052] “A dry-condition reflectivity threshold R.sub.t may be defined to detect a wet surface 150”; [0054] “computer 110 may be programmed to determine whether a surface 150 is wet at, e.g., a point 170 with location coordinates (x, y, z) based on a reflectivity R of the surface 150”; [0045] “moisture or wetness may be caused by other liquids alone or in combination with one another and/or water, e.g., oil, etc., and/or vapors”).
Regarding Claim 21: Arita, in view of Ding and Herman, teaches the system of claim 17. In this combination, Herman further teaches wherein causing the vehicle control system to actuate the vehicle control mechanism based on the perception of the environment associated with the vehicle comprises: causing the vehicle control system to control the vehicle to perform at least one of: speeding up, slowing down, turning left, turning right, turning at a predetermined degree of angle, signaling, pulling to a side of the road, or gradually stopping the vehicle based on the perception of the environment associated with the vehicle (Fig. 9, method 900, block 935 determining whether the surface is wet and if the answer is yes, proceeding to block 940 which adjusts vehicle operation; [0029] “The actuators 120 may be used to control braking, acceleration, and steering of the first vehicle 100”).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Arita (US 20030218919 A1), in view of Ding (US 20190339386 A1), further in view of Herman (US 20210009137 A1), further in view of Zhu (US 20200310451 A1). Arita, in view of Ding and Herman, teaches the system of claim 18. They do not teach wherein the road shape perception comprises a perception of at least one of: an uphill road shape, a downhill road shape, a slope varying road shape, a left winding road shape, and a right winding road shape.
Zhu teaches this limitation in paragraph [0035]: “perception can include the lane configuration (e.g., straight or curve lanes),” and “The lane configuration includes information describing a lane or lanes, such as, for example, a shape of the lane (e.g., straight or curvature), a width of the lane, how many lanes in a road, one-way or two-way lane, merging or splitting lanes, exiting lane, etc.”
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the perception of the vehicle environment taught by Arita, Ding, and Herman, such that the perception includes road shape, as taught by Zhu. Taking into account the shape of the road, such as the curvature or configuration of lanes, is critical for autonomous driving, specifically for path and speed planning (Zhu, [0003]).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Arita (US 20030218919 A1), in view of Ding (US 20190339386 A1), further in view of Herman (US 20210009137 A1), further in view of Mahara (US 20220057520 A1). Arita, in view of Ding and Herman, teaches the system of claim 17. They do not teach: wherein causing the vehicle control system to actuate the vehicle control mechanism based on the perception of the environment associated with the vehicle comprises: causing the vehicle control system to dynamically adjust a region of interest of the LiDAR system, wherein the LiDAR system is configured to scan the region of interest more densely than other regions.
Mahara teaches: causing the vehicle control system to dynamically adjust a region of interest of the LiDAR system, wherein the LiDAR system is configured to scan the region of interest more densely than other regions ([0060] “the control section 10 changes the distance measurement conditions in such a manner as to increase the sampling frequency and/or reduce the resolution” for closer objects; Fig. 8, regions with closer distance are scanned more densely).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further modify the vehicle controls taught by Arita, Ding, and Herman, such that the vehicle controls include scanning a region of interest more densely. This would be beneficial because it enables the system to acquire the accurate distance measurements for both close and far distances (Mahara, [0060-0061]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISABELLE LIN BOEGHOLM whose telephone number is (571)270-0570. The examiner can normally be reached Monday-Thursday 7:30am-5pm, Fridays 8am-12pm.
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, Yuqing Xiao can be reached at (571) 270-3603. 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.
/ISABELLE LIN BOEGHOLM/ Examiner, Art Unit 3645
/YUQING XIAO/ Supervisory Patent Examiner, Art Unit 3645