DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
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) 1-20,22-29 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200300984 A1 (MATSUURA) in view of US 20210094538 A1 (Beller).
Claim 1,25 and 29 (mutatis mutandis), Matsuura teaches the method for determining measurement information based on a multitude of measurement values from a measurement value range, the method comprising:
acquiring a frequency distribution of a plurality of measurement values, wherein the measurement values of the frequency distribution are each allocated to one class of a plurality of classes of the frequency distribution, and wherein a frequency value of a class describes a number of measurement values allocated to the class (para 240-267 note and figure 30 A,B note frequency and time (distance))
dividing the frequency distribution into several regions, wherein one of the regions each represents an interval of the measurement value range and comprises one or several classes of the frequency distribution (para 240-267 note and figure 30 A,B note bins),
selecting one class each of a respective region as a selected class of the respective region based on a selection rule, wherein one region feature each is allocated to the regions based on the selection rule (para 240-267 note maximum frequency bin is chosen),
determining a probability value for one of the selected classes based on the region features, wherein the probability value represents an estimation for the probability with which the selected class represents a value of a useful signal, (para 240-267 note probability).
Matsuura teaches machine learning for faster calculation but fails to explicitly teach determining the probability value is based on methods of machine learning. However, Beller teaches wherein determining the probability value is based on methods of machine learning (para 138).
It would have been obvious to have combined the references of Matsuura and Beller and modify the method such that determining the probability value is based on methods of machine learning because this will improve performance (Matsuura para 275)
Claim 2. (Original) Matsuura as modified in view of Beller teaches the method according to claim 1, wherein the frequency distribution is part of a series of frequency distributions of a respective plurality of measurement values, wherein the method further comprises comparing the selected classes to the selected classes of a previous frequency distribution of the series of frequency distributions to adapt or maintain one or several of the region features depending on the comparison, and providing the region features for determining the probability value (Matsuura para 240-267 note comparison ).
Claim 3. (Original) Matsuura as modified in view of Beller teaches the method according to claim 2, wherein comparing the selected classes to the selected classes of the previous frequency distribution of the series of frequency distributions comprises comparing a position of one of the selected classes of the frequency distribution to the positions of one or several of the selected of the previous frequency distribution (Matsuura para 240-259 note position).
Claim 4. (Original) Matsuura as modified in view of Beller teaches the method according to claim 2, wherein comparing the selected classes to the selected classes of a previous frequency distribution of the series of frequency distributions comprises selectively adapting the region feature of the region of a selected class by considering a previous selected class, if the previous selected class is within a correlation interval, wherein the previous selected class is one of the selected classes of the previous frequency distribution (Matsuura para 240-267).
Claim 5. (Original) Matsuura as modified in view of Beller teaches the method according to claim 3, wherein comparing the selected classes to the selected classes of a previous frequency distribution of the series of frequency distribution comprises determining a comparison position based on positions of one or several of the selected classes of several previous frequency distributions of the series of frequency distributions and selectively adapting the region feature of the region of one of the selected classes of the frequency distribution by considering the selected classes used for determining the comparison position if the comparison position is within a correlation interval (Matsuura see figure 30A and 30 B and para 240-250).
Claim 6. (Original) Matsuura as modified in view of Beller teaches the method according to claim 2, wherein adapting one of the region features is based on an adaptation coefficient, wherein the adaptation coefficient is based on the probability value and/or the frequency value and/or a position of one or several selected classes of the previous frequency distribution (Matsuura see figure 30A and 30 B and para 240-267).
Claim 7. (Original) Matsuura as modified in view of Beller teaches the method according to claim 4, wherein adapting the region feature is based on an adaptation coefficient, wherein the adaptation coefficient and/or the correlation interval is based on the probability value and/or the frequency value and/or a position of the considered previous selected class (Matsuura see figure 30A and 30 B and para 240-250).
Claim 8. (Currently Amended) Matsuura as modified in view of Beller teaches the method according to claim 6, wherein comparing the selected classes to the selected classes of a previous frequency distribution of the series of frequency distribution comprises determining a comparison position based on positions of one or several of the selected classes of several previous frequency distributions of the series of frequency distributions and determining the adaption coefficient based on the probability values and/or the frequency values and/or the positions of the selected classes used for determining the comparison position (Matsuura see figure 30A and 30 B and para 240-267).
Claim 9. (Original) Matsuura as modified in view of Beller teaches the method according to claim 5, wherein adapting the region feature is based on an adaptation coefficient, wherein the adaption coefficient and/or the correlation interval is based on the probability value and/or the frequency value and/or a position of the selected classes used for determining the comparison position (Matsuura see figure 30A and 30 B and para 240-250).
Claim 10. (Original) Matsuura as modified in view of Beller teaches the method according to claim 6, wherein the method further comprises providing the adaptation coefficient as part of the measurement information (Matsuura see figure 30A and 30 B and para 240-267).
Claim 11. (Currently Amended) Matsuura as modified in view of Beller teaches the method according to claim 4, wherein a position of the correlation interval in the measurement value range is based on a position of the selected class in the measurement value range and wherein a width of the correlation interval is based on an expected change of the value of the useful signal (Matsuura see figure 30A, 30 B and 31 and para 240-267 note width).
Claim 12. (Original) Matsuura as modified in view of Beller teaches the method according to claim 4, wherein a position of the correlation interval in the measurement value range is based on a position of the selected class in the measurement value range and on an expected change of the value of the useful signal and wherein a width of the correlation interval is based on the expected change of the value of the useful signal (Matsuura see figure 30A 30 B and 31 and para 240-267 note width).
Claim 13. (Original) Matsuura as modified in view of Beller teaches the method according to claim 4, wherein the method further comprises determining the correlation interval and/or the adaptation coefficient by using an artificial neuronal network (Matsuura para 275 note deep learning).
Claim 14. (Original) Method according to one of the preceding claims, wherein the method further comprises determining, from the selected classes, the one with the highest probability value as a useful signal class and providing a position in the measurement value range represented by the useful signal class as part of the measurement information (Matsuura para 257 note probability).
Claim 15. (Original) Matsuura as modified in view of Beller teaches the method according to claim 14, wherein the method further comprises providing the probability value of the useful signal class as part of the measurement information (Matsuura para 257 note probability).
Claim 16. (Original) Matsuura as modified in view of Beller teaches the method according to claim 1, wherein selecting the selected classes comprises selecting that class of one of the regions as selected class of the region that has the highest frequency value and wherein the region feature allocated to the region is based on the frequency value of the selected class of the region (Matsuura see figure 30A and 30 B and para 240-267note maximum frequency and peak).
Claim 17. (Original) Matsuura as modified in view of Beller teaches the method according to claim 1, wherein the regions are equidistant and adjacent (Matsuura see figure 30A and 30 B and para 240-267).
Claim 18. (Original) Matsuura as modified in view of Beller teaches the method according to claim 1, wherein a measurement value of the multitude of measurement values represents a time period between emitting a light pulse and detecting a photon, wherein the measurement value either represents a useful signal value when the photon is based on the light pulse or represents a background signal value and wherein the useful signal is based on one or several useful signal values (Matsuura see figure 30A and 30 B and para 240-267 note noise).
Claim 19. (Original) Matsuura as modified in view of Beller teaches the method according to claim 18, wherein dividing the frequency distribution into the several regions comprises selecting a width of one of the regions such that that class of the region into which a measurement value falling into that region falls with the highest probability represents a class of the useful signal if the useful signal falls into the region (Matsuura see figure 30A and 30 B and para 240-267 note noise).
Claim 20. (Original) Matsuura as modified in view of Beller teaches the method according to claim 1, wherein the methods of machine learning comprise an artificial neuronal network and wherein the method comprises training the artificial neuronal network based on the region features allocated to the regions of the frequency distribution (Matsuura para 240-267 and para 275 note deep learning).
Claim 22. (Original) Matsuura as modified in view of Beller teaches the method according to claim 1, wherein the multitude of measurement values represents a series of measurement values and wherein the method further comprises collecting a multitude of successive measurement values of the series of measurement values to acquire a frequency distribution of the series of frequency distributions (Matsuura see figure 30A and 30 B and para 240-267 note noise).
Claim 23. (Original) Matsuura as modified in view of Beller teaches the method according to claim 1, comprising: outputting a light pulse by means of a light source, determining a time period between outputting a light pulse and detecting a photon by means of a detector and providing the time period as a measurement value of the multitude of measurement values (Matsuura para 79-88).
Claim 24. (Original) Matsuura as modified in view of Beller teaches the method according to claim 1, wherein the method comprises acquiring a plurality of measurement value series in parallel and determining a contribution to the measurement information, each based on the respective frequency distributions of a respective plurality of measurement values of the plurality of measurement values, wherein the method comprises determining, from the selected classes of the respective frequency distribution, the selected class having the highest probability value as a useful signal class and providing a position in the measurement value range represented by the useful signal class as part of the respective contribution to the measurement information (Matsuura para 240-267 note probability).
Claim 26. (Original) Matsuura as modified in view of Beller teaches the LiDAR device, comprising the apparatus according to claim 25 and further:
a light source configured to emit a light pulse and to provide a first signal in connection with emitting the light pulse, a detector configured to detect a photon and to provide a second signal as a result of detecting a photon, a correlator configured to determine, based on the first signal and the second signal, a time period between emitting the light pulse and detecting the photon and to provide the time period as a measurement value of the multitude of measurement values (para 79-88), wherein the measurement value represents a useful signal value when the photon is based on an echo of the light pulse, wherein the useful signal is based on one or several useful signal values and wherein the measurement information comprises a position of the selected class of the frequency distribution with the highest probability value (Matsuura para 240-267).
Claim 27. (Original) Matsuura as modified in view of Beller teaches the LiDAR device according to claim 26, wherein the region feature of the region of a selected class is selectively adapted by considering a previous selected class, if the previous selected class is within a correlation interval, wherein the previous selected class is one of the selected classes of the previous frequency distribution and wherein a position of the correlation interval in the measurement value range and/or a width of the correlation interval is based on an expected change of the value of the useful signal and wherein the expected change is based on a velocity and/or acceleration of the LiDAR device (Matsuura para 240-267 and para 306-307 note lidar).
Claim 28. (Original) Matsuura as modified in view of Beller teaches the LiDAR device according to claim 26, wherein the LiDAR device comprises a plurality of detector units, wherein the LiDAR device is configured to acquire a multitude of measurement values by using a respective detector unit, wherein the apparatus for determining the measurement information is configured to determine, based on the respective multitude of measurement values, a contribution to the measurement information allocated to the respective detector unit (Matsuura para 79-88 note light receiving element and pin photodiode and avalanche photodiode).
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) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200300984 A1 (MATSUURA) in view of US 20210094538 A1 (Beller) further in view of US 20230316528 A1 (Phelps).
Claim 21. (Original) Matsuura as modified in view of Beller teaches the method according to claim 1. Matsuura fails to explicitly teach but Phelps teaches wherein the method further comprises deconvolving the frequency distribution with one or several convolution kernels prior to selecting the selected class (para 100).
It would have been obvious to have combined the references of Matsuura, Beller and Phelps and modify the method such that the method further comprises deconvolving the frequency distribution with one or several convolution kernels prior to selecting the selected class because this will reduce blurring effect (Phelps abstract).
Response to Arguments
Applicant's arguments filed 12/27/2025 have been fully considered but they are not persuasive. Applicant’s argument regarding dividing the frequency distribution into several regions is not persuasive. Applicant describes region as an interval of the measurement value range (see summary of instant application). Matsuura teaches histograms and the bins are the regions. The definition of histogram is “A histogram is a visual representation of the distribution of quantitative data. To construct a histogram, the first step is to "bin" (or "bucket") the range of values— divide the entire range of values into a series of intervals—and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) are adjacent and are typically (but not required to be) of equal size.” (see teaching reference Histogram). Dividing the entire range of values into a series of intervals is inherent with histogram. The x-axis of figure 30A and 0 B shows the frequency distribution.
Rest of the arguments are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/SANJIDA NASER/Examiner, Art Unit 3645
/ISAM A ALSOMIRI/Supervisory Patent Examiner, Art Unit 3645