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 § 102
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 following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 5-7, 9-12, 14-17, and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pacala US20220291387.
Regarding independent claim 1, Pacala discloses, in Figures 1-21,
A system (Pacala; Fig. 1-21), comprising: a light source (Pacala; light transmission module 240 that transmits light pulses 249) configured to generate an emitted pulse of light (Pacala; light pulses 249); a receiver (Pacala; light sensing module 230 with corresponding sensor array 510 that comprises photosensors 515 which further comprise photodetectors 525) configured to detect at least a portion of the emitted pulse of light scattered by an external target (Pacala; object 408) and provide a plurality of different measurement signals (Pacala; there is a corresponding measurement signal for each photodetector 525 and a corresponding micro-optic receiver channel; [0050] “array of micro-optic receiver channels”) for the detected emitted pulse of light; and a processor configured to analyze the plurality of different measurement signals to identify the plurality of different measurement signals as corresponding to the same external target (Pacala; Fig. 2; the assembly of processors 238, 245, 258 along with LIDAR AI co-processor 1510, classifier 1514, signal processor 1516, and controller 1450; [0164] controller 1450; [0190] “For instance, one can use the statistics for the depth values of the group to determine which pixels correspond to a same object, e.g., a group of pixels having depth values within a threshold of each other. Corresponding to a same object is one way of correlating two pixels. Such a classification for a group can be used to increase accuracy of depth values of a final lidar image, fill in missing data, provide greater resolution, etc.”; [0191] “classification information can indicate which lidar pixels of a lidar image correspond to the same object”; [0206] “Classifier 1514 can use the depth values from one or more lidar images to determine which pixels correspond to a same object. The use of the depth values can increase the accuracy of the classifier, as depth values for neighboring points on a same object will not change drastically from one pixel to a neighboring pixel, e.g., a neighboring position on the rectilinear grid. The use of depth values of neighboring lidar pixels allows provides an increased confidence in identifying two pixels as being from a same object, e.g., compared to just using color values.”; [0207] “Once a group of lidar pixels are identified as corresponding to a same object, the depth values may be smoothed just for that group of pixels, e.g., as performed by lidar image processor 1410, which can received classification information from lidar AI co-processor 1510.”) and combine the identified plurality of different measurement signals to determine a measurement for the external target (Pacala; [0164] “determine depth values from measurements using the array of photosensors”; [0165] “the results can be combined”; [0207] “Once a group of lidar pixels are identified as corresponding to a same object, the depth values may be smoothed just for that group of pixels, e.g., as performed by lidar image processor 1410, which can received classification information from lidar AI co-processor 1510.”) (Pacala; see labeled Figure 5 below).
PNG
media_image1.png
768
798
media_image1.png
Greyscale
Pacala Figure 5.
Regarding claim 2, Pacala discloses The system of claim 1, wherein identifying the plurality of different measurement signals as corresponding to the same external target includes determining one or more distance values between the plurality of different measurement signals (Pacala; [0164] “controller 1450 (or other ranging circuit) can be connected to the array of photosensors and configured to determine depth values from measurements using the array of photosensors to form a lidar image comprising a grid of lidar pixels ( e.g., forming a rectilinear frame of an environment during a measurement interval), and periodically output lidar images. Each lidar image can be generated during a different measurement interval and comprise rows and columns of lidar pixels.”).
Regarding claim 3, Pacala discloses The system of claim 2, wherein identifying the plurality of different measurement signals as corresponding to the same external target further includes comparing the determined one or more distance values with one or more threshold values (Pacala; [0190] “For instance, one can use the statistics for the depth values of the group to determine which pixels correspond to a same object, e.g., a group of pixels having depth values within a threshold of each other.”).
Regarding claim 5, Pacala discloses The system of claim 2, wherein determining the one or more distance values includes analyzing a property associated with the plurality of different measurement signals (Pacala; [0070] “The pulse strength can be used to estimate the target reflectivity”).
Regarding claim 6, Pacala discloses The system of claim 5, wherein the property associated with the plurality of different measurement signals includes a reflectance value (Pacala; [0070] “The pulse strength can be used to estimate the target reflectivity”).
Regarding claim 7, Pacala discloses The system of claim 1, wherein combining the identified plurality of different measurement signals to determine the measurement for the external target includes determining a corresponding weight to apply to each corresponding measurement signal of the identified plurality of different measurement signals (Pacala; [0175] “Gaussian weighting” and “determine kernel weights or “sameness” for each pixel with respect to a center pixel, so as to provide a filtered value for the center pixel”; [0208] “The use of the classification ( e.g., semantic labeling) could be done as a second pass using a filter kernel that accumulates signal values according to weights defined by a kernel function (e.g., Gaussian) as described herein.”).
Regarding claim 9, Pacala discloses The system of claim 1, wherein combining the identified plurality of different measurement signals to determine the measurement for the external target includes applying a machine learning model using the identified plurality of different measurement signals as input features to predict the measurement for the external target (Pacala; [0187] using AI machine learning models “to identify and classify objects” and “such information can be used to update such images”).
Regarding claim 10, Pacala discloses The system of claim 1, wherein a first channel of one or more detectors of the receiver provides a first measurement signal of the plurality of different measurement signals for the detected emitted pulse of light, a second channel of the one or more detectors of the receiver provides a second measurement signal of the plurality of different measurement signals for the detected emitted pulse of light, and the first channel and the second channel are different channels (Pacala; see labeled Figure 5 above).
Regarding claim 11, Pacala discloses The system of claim 10, wherein the first channel and the second channel correspond to a same detector of the one or more detectors of the receiver (Pacala; see labeled Figure 5 above).
Regarding claim 12, Pacala discloses The system of claim 10, wherein the first channel and the second channel correspond to different detectors of the one or more detectors of the receiver (Pacala; see labeled Figure 5 above).
Regarding claim 14, Pacala discloses The system of claim 1, wherein the determined measurement for the external target is a range or reflectance value (Pacala; [0070] “to compute the distance to the reflecting surface”).
Regarding claim 15, Pacala discloses The system of claim 1, wherein the determined measurement for the external target is a shape, a velocity, or a surface angulation value (Pacala; [0070] “to compute the relative velocity between the sensor and the reflecting surface”).
Regarding independent claim 16, Pacala discloses the invention substantially the same as described above in reference to independent claim 1, and A method (Pacala; Fig. 1-21), comprising: emitting light (Pacala; light pulses 249) from a light source (Pacala; light transmission module 240 that transmits light pulses 249); scanning the emitted light across a field of view (Pacala; [0048] “field of view”); using a first detector (Pacala; see labeled Figure 5 above) to detect a return light pulse corresponding to the emitted light scattered by a target (Pacala; object 408) located downrange and providing a first measurement signal (Pacala; there is a corresponding measurement signal for each photodetector 525 and a corresponding micro-optic receiver channel; [0050] “array of micro-optic receiver channels”); using a second detector (Pacala; see labeled Figure 5 above) to detect the return light pulse and provide a second measurement signal (Pacala; there is a corresponding measurement signal for each photodetector 525 and a corresponding micro-optic receiver channel; [0050] “array of micro-optic receiver channels”); identifying the first measurement signal and the second measurement signal as corresponding to the same target (Pacala; Fig. 2; the assembly of processors 238, 245, 258 along with LIDAR AI co-processor 1510, classifier 1514, signal processor 1516, and controller 1450; [0164] controller 1450; [0190] “For instance, one can use the statistics for the depth values of the group to determine which pixels correspond to a same object, e.g., a group of pixels having depth values within a threshold of each other. Corresponding to a same object is one way of correlating two pixels. Such a classification for a group can be used to increase accuracy of depth values of a final lidar image, fill in missing data, provide greater resolution, etc.”; [0191] “classification information can indicate which lidar pixels of a lidar image correspond to the same object”; [0206] “Classifier 1514 can use the depth values from one or more lidar images to determine which pixels correspond to a same object. The use of the depth values can increase the accuracy of the classifier, as depth values for neighboring points on a same object will not change drastically from one pixel to a neighboring pixel, e.g., a neighboring position on the rectilinear grid. The use of depth values of neighboring lidar pixels allows provides an increased confidence in identifying two pixels as being from a same object, e.g., compared to just using color values.”; [0207] “Once a group of lidar pixels are identified as corresponding to a same object, the depth values may be smoothed just for that group of pixels, e.g., as performed by lidar image processor 1410, which can received classification information from lidar AI co-processor 1510.”); and combining the first measurement signal and the second measurement signal to determine a fused measurement for the target (Pacala; [0164] “determine depth values from measurements using the array of photosensors”; [0165] “the results can be combined”; [0207] “Once a group of lidar pixels are identified as corresponding to a same object, the depth values may be smoothed just for that group of pixels, e.g., as performed by lidar image processor 1410, which can received classification information from lidar AI co-processor 1510.”).
Regarding claim 17, Pacala discloses The method of claim 16, wherein identifying the first measurement signal and the second measurement signal as corresponding to the same target includes determining one or more distance values between the first measurement signal and the second measurement signal (Pacala; [0164] “controller 1450 (or other ranging circuit) can be connected to the array of photosensors and configured to determine depth values from measurements using the array of photosensors to form a lidar image comprising a grid of lidar pixels ( e.g., forming a rectilinear frame of an environment during a measurement interval), and periodically output lidar images. Each lidar image can be generated during a different measurement interval and comprise rows and columns of lidar pixels.”) and comparing the determined one or more distance values with one or more threshold values (Pacala; [0190] “For instance, one can use the statistics for the depth values of the group to determine which pixels correspond to a same object, e.g., a group of pixels having depth values within a threshold of each other.”).
Regarding claim 19, Pacala discloses The method of claim 16, wherein combining the first measurement signal and the second measurement signal to determine the fused measurement for the target includes determining a first weight to apply to the first measurement signal and a second weight to apply to the second measurement signal (Pacala; [0175] “Gaussian weighting” and “determine kernel weights or “sameness” for each pixel with respect to a center pixel, so as to provide a filtered value for the center pixel”; [0208] “The use of the classification ( e.g., semantic labeling) could be done as a second pass using a filter kernel that accumulates signal values according to weights defined by a kernel function (e.g., Gaussian) as described herein.”).
Regarding independent claim 20, Pacala discloses the invention substantially the same as described above in reference to independent claims 1 and 16, and A system (Pacala; Fig. 1-21), comprising: a light source (Pacala; light transmission module 240 that transmits light pulses 249) configured to emit light (Pacala; light pulses 249); a scanner (Pacala; the assembly of emitter array 242 and Tx optical system 244 of Tx module 240) configured to scan the emitted light across a field of view (Pacala; [0048] “field of view”); a first detector (Pacala; see labeled Figure 5 above) of a receiver (Pacala; light sensing module 230 with corresponding sensor array 510 that comprises photosensors 515 which further comprise photodetectors 525) configured to detect a return light pulse corresponding to the emitted light scattered by an external target (Pacala; object 408) located downrange to provide a first measurement signal (Pacala; there is a corresponding measurement signal for each photodetector 525 and a corresponding micro-optic receiver channel; [0050] “array of micro-optic receiver channels”); a second detector (Pacala; see labeled Figure 5 above) of the receiver configured to detect the return light pulse to provide a second measurement signal (Pacala; there is a corresponding measurement signal for each photodetector 525 and a corresponding micro-optic receiver channel; [0050] “array of micro-optic receiver channels”) and a third measurement signal (Pacala; there is a corresponding measurement signal for each photodetector 525 and a corresponding micro-optic receiver channel; [0050] “array of micro-optic receiver channels”), wherein the second measurement signal and the third measurement signal are different; and a processor configured to identify the first measurement signal, the second measurement signal, and the third measurement signal as corresponding to the same external target (Pacala; Fig. 2; the assembly of processors 238, 245, 258 along with LIDAR AI co-processor 1510, classifier 1514, signal processor 1516, and controller 1450; [0164] controller 1450; [0190] “For instance, one can use the statistics for the depth values of the group to determine which pixels correspond to a same object, e.g., a group of pixels having depth values within a threshold of each other. Corresponding to a same object is one way of correlating two pixels. Such a classification for a group can be used to increase accuracy of depth values of a final lidar image, fill in missing data, provide greater resolution, etc.”; [0191] “classification information can indicate which lidar pixels of a lidar image correspond to the same object”; [0206] “Classifier 1514 can use the depth values from one or more lidar images to determine which pixels correspond to a same object. The use of the depth values can increase the accuracy of the classifier, as depth values for neighboring points on a same object will not change drastically from one pixel to a neighboring pixel, e.g., a neighboring position on the rectilinear grid. The use of depth values of neighboring lidar pixels allows provides an increased confidence in identifying two pixels as being from a same object, e.g., compared to just using color values.”; [0207] “Once a group of lidar pixels are identified as corresponding to a same object, the depth values may be smoothed just for that group of pixels, e.g., as performed by lidar image processor 1410, which can received classification information from lidar AI co-processor 1510.”) and combine the first measurement signal, the second measurement signal, and the third measurement signal to determine a fused measurement for the external target (Pacala; [0164] “determine depth values from measurements using the array of photosensors”; [0165] “the results can be combined”; [0207] “Once a group of lidar pixels are identified as corresponding to a same object, the depth values may be smoothed just for that group of pixels, e.g., as performed by lidar image processor 1410, which can received classification information from lidar AI co-processor 1510.”) (Pacala; see labeled Figure 5 above).
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 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.
Claim(s) 4 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pacala in view of Gorodetsky et al. US20220020170.
Regarding claim 4, Pacala discloses The system of claim 2, wherein identifying the plurality of different measurement signals as corresponding to the same external target further includes applying using the determined one or more distance values (Pacala; [0190] “For instance, one can use the statistics for the depth values of the group to determine which pixels correspond to a same object, e.g., a group of pixels having depth values within a threshold of each other. Corresponding to a same object is one way of correlating two pixels. Such a classification for a group can be used to increase accuracy of depth values of a final lidar image, fill in missing data, provide greater resolution, etc.”; [0206] “Classifier 1514 can use the depth values from one or more lidar images to determine which pixels correspond to a same object.”).
Pacala does not disclose applying a cost function using the determined one or more distance values.
Gorodetsky teaches applying a cost function using the determined one or more distance values (Gorodetsky; determine cost function step 1015 in flowchart Fig. 10; [0063] “to determine a cost function representing a degree of agreement between the projections from block 1010… When the cost function does not decrease between iterations, the most recent candidate depth processed is employed to generate the combined position, at block 1025.”; [0064] “The cost function determined at block 1015 may be, for example, the sum of distances between the centroids of the projections 1104 (that is, a sum of three distances).”).
It would have been obvious to one having ordinary skill at the effective filing date of the invention to modify the system as taught by Pacala to include applying a cost function as taught by Gorodetsky for the purpose of determining the highest degree of agreement between distance/ranging outputs by minimizing the cost function (Gorodetsky; [0063] “to determine a cost function representing a degree of agreement between the projections from block 1010… When the cost function does not decrease between iterations, the most recent candidate depth processed is employed to generate the combined position, at block 1025.”).
Regarding claim 18, Modified Pacala teaches the invention substantially the same as described above in reference to claim 4, and The method of claim 16, wherein identifying the first measurement signal and the second measurement signal as corresponding to the same target includes determining one or more distance values between the first measurement signal and the second measurement signal (Pacala; [0164] “controller 1450 (or other ranging circuit) can be connected to the array of photosensors and configured to determine depth values from measurements using the array of photosensors to form a lidar image comprising a grid of lidar pixels ( e.g., forming a rectilinear frame of an environment during a measurement interval), and periodically output lidar images. Each lidar image can be generated during a different measurement interval and comprise rows and columns of lidar pixels.”) and applying a cost function using the determined one or more distance values (Gorodetsky; determine cost function step 1015 in flowchart Fig. 10; [0063] “to determine a cost function representing a degree of agreement between the projections from block 1010… When the cost function does not decrease between iterations, the most recent candidate depth processed is employed to generate the combined position, at block 1025.”; [0064] “The cost function determined at block 1015 may be, for example, the sum of distances between the centroids of the projections 1104 (that is, a sum of three distances).”).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pacala in view of Stanley-Marbell US20240241223.
Regarding claim 8, Pacala discloses The system of claim 7, wherein the determined corresponding weight is based on an estimated variance value determined for the corresponding measurement signal of the identified plurality of different measurement signals (Pacala; Gaussian distribution necessarily involves a variance value; [0175] “Gaussian weighting” and “determine kernel weights or “sameness” for each pixel with respect to a center pixel, so as to provide a filtered value for the center pixel”; [0208] “The use of the classification ( e.g., semantic labeling) could be done as a second pass using a filter kernel that accumulates signal values according to weights defined by a kernel function (e.g., Gaussian) as described herein.”).
Pacala does not disclose wherein the determined corresponding weight is based on an inverse of a corresponding estimated variance value determined for the corresponding measurement signal of the identified plurality of different measurement signals.
Stanley-Marbell teaches applying a Noise Model which applies an inverse proportion in relation to a variance for the purpose of optimizing the balance between transmission cost and bit errors at the receiver/detector (Stanley-Marbell; [0319] “the Noise Model may vary the probability (p) of a bit error occurring for a given a bit modulation (Δ) by changing the value of the variance (σ.sup.2) of the Gaussian distribution in inverse proportion to the magnitude of the bit modulation (Δ)”; [0320] “In this way, the computer finds the ‘best’ signal bit modulation which balances the need to reduce the transmission ‘cost’ enough, while not incurring unacceptable bit errors at the receiver.”).
It would have been obvious to one having ordinary skill at the effective filing date of the invention to modify the determination as taught by Pacala to be based on an inverse of a corresponding estimated variance value as taught by Stanley-Marbell for the purpose of optimizing the balance between transmission cost and bit errors at the receiver/detector (Stanley-Marbell; [0320] “In this way, the computer finds the ‘best’ signal bit modulation which balances the need to reduce the transmission ‘cost’ enough, while not incurring unacceptable bit errors at the receiver. The balance is struck by finding which signal bit modulations to apply at the transmitter modulation unit 8 to the signal bits representing of the data to be transmitted (i.e. to which bits and by how much), with the expectation that some bit errors will occur at the receiver, but intelligently accepting bit errors in bits that do not have a significant impact on the overall accuracy of the resulting data interpreted by the receiver.”).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pacala in view of Gaalema et al. US20200182968.
Regarding claim 13, Pacala discloses The system of claim 1, wherein two or more channels are associated with a first detector of one or more detectors of the receiver, and each of the two or more channels is configured with an amplification setting (Pacala; amplification configuration corresponds to bias voltage configuration; [0075] “SPADs are normally biased with a biased voltage above the breakdown voltage.”; see labeled Figure 5 above).
Pacala does not disclose each of the two or more channels is configured with a different amplification setting.
Gaalema teaches a detectors with different amplifier gain settings (Gaalema; [0147]; [0149] “one or more detectors may be associated with one or more different gain values. For example, in FIG. 19, the gain associated with detector 340-la may be configured to be lower than the gain associated with detector 340-lb.” and “the gain of a detector may depend on the reverse bias voltage applied to the detector, where a larger reverse bias voltage results in a larger gain”).
It would have been obvious to one having ordinary skill at the effective filing date of the invention to modify the amplification setting as taught by Pacala so that they are different between the channels as taught by Gaalema for the purpose of creating channels that have different optimal detection ranges that balance the risk of saturation and detection distance/range (Gaalema; [0149] relatively low gain is better suited for relatively close-range targets while relatively high gain is better suited for relatively intermediate/far-range targets).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Campbell US20170131388 teaches a lidar system.
LaChapelle US20180284240 teaches using detectors with different gains in a lidar system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN MALIKASIM whose telephone number is (313)446-6597. The examiner can normally be reached M-F; 8 am - 5 pm (CST).
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, Amy Weisberg can be reached at 571-270-5500. 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.
/JONATHAN MALIKASIM/ Primary Examiner, Art Unit 3612 1/15/26