CTNF 18/523,163 CTNF 79015 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-06 AIA 15-10-15 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 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. DETAILED ACTION Claims status Claims 1-20 are pending as the applicant filed Preliminary Amendment on 11/29/2023. Claim Rejections - 35 USC § 112 07-30-02 AIA 2. The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 3, 13, 4, 14, 8 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The following terms were not sure are part of the claim: In claim 3, 13, “(a number of channels)” in claim 4, 14, “(a number of pixels)”, in claim 8, 18 "pitch-density function" Note: In view of the PTO compact prosecution, the Examiner notes that due to the indefiniteness issues described above all consideration of the merits of the claims in view of prior art is as best understood. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1, Step 1 the claim is a process (or machine) ( Yes ), Step 2A Prong One , does the claim recite an abstract idea? current claim related to a method for constructing a LiDAR semantic segmentation model through two-step domain adaptation, the method comprising: converting, by a processor, a first LiDAR data set of a first domain to obtain a second LiDAR data set of a second domain; performing, by the processor, a machine learning with the second LiDAR data set as training data to obtain a first semantic segmentation model of an artificial intelligence model; and performing, by the processor, a feature domain adaptation for the first semantic segmentation model utilizing target data to obtain a second semantic segmentation model appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes . Step 2A Prong Two , is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? There is no more additional element. Step 2A Prong Two: NO . Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? There is no more additional element. Step 2B: No. claim 1 not eligible. Claim 11, Step 1 the claim is a process (or machine) ( Yes ), Step 2A Prong One , does the claim recite an abstract idea? current claim related to a LiDAR-based object perception apparatus, comprising: wherein the LiDAR semantic segmentation model is constructed by obtaining a second LiDAR data set of a second domain by converting a first LiDAR data set of a first domain, obtaining a first semantic segmentation model of an artificial intelligence model by performing a machine learning with the second LiDAR data set as training data, and obtaining a second semantic segmentation model by performing a feature-domain adaptation for the first semantic segmentation model utilizing target data appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes . Step 2A Prong Two , is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? There is no more additional element. Step 2A Prong Two: NO . Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional elements of a LiDAR sensor that obtains cloud points for a surrounding environment; a computer-readable recording medium storing a computer program, which when executed causes a segmentation on the cloud points according to a LiDAR semantic segmentation model; and a processor executing the computer program are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application Step 2A Prong Two: NO . Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? There is no more additional element. Step 2B: No. claim 11 not eligible. Claim 2 related to wherein the converting of the first LiDAR data set includes converting, by the processor, vertical coordinate values of the first LiDAR data set according to vertical coordinates of the second domain appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 2 not eligible. Claim 3 related to wherein the converting of the first LiDAR data set includes converting, by the processor, the first LiDAR data set into a first range view image having a first horizontal resolution (a number of pixels) and a first vertical resolution (a number of channels), and converting, by the processor, the first range view image into a second range view image having a horizontal resolution and a vertical resolution corresponding to the second domain appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 3 not eligible. Claim 4 related to wherein the converting of the first LiDAR data set further includes obtaining, by the processor, the second range view image by mapping, by the processor, the first range view image to a range view image frame of a second horizontal resolution (a number of pixels appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 4 not eligible. Claim 5 related to wherein the first semantic segmentation model includes a deep learning network which has n (integer) encoder layers, and the second horizontal resolution is a number above a number obtained by dividing 360 degrees by a horizontal scan resolution (angle) of the second domain among multiples of 2 to a power of n appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 5 not eligible. Claim 6 related to wherein the obtaining of the second range view image, in response to the first horizontal resolution being greater than the second horizontal resolution and a plurality of pixels of the first range view image are mapped to one pixel of the second range view image, includes performing, by the processor, a mapping by selecting, by the processor, a LiDAR point having a smaller distance coordinate value among LiDAR points corresponding to the plurality of pixels appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 6 not eligible. Claim 7 related to wherein the converting of the first LiDAR data set further includes obtaining, by the processor, the second range view image by converting, by the processor, the first range view image into a range view image of a second vertical resolution (a number of channels) appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 7 not eligible. Claim 8 related to wherein the converting of the first LiDAR data set further includes converting, by the processor, the first range view image into a "pitch-density function" domain, dividing, by the processor, a pitch axis into equal parts by the second vertical resolution (a number of channels), and obtaining, by the processor, the second range view image from a density function value corresponding to the equal parts appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 8 not eligible. Claim 9 related to wherein a density function value for an equal part that does not have the corresponding density function value among the equal parts is determined to be 0 (zero) appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 9 not eligible. Claim 10 related to wherein the converting of the first LiDAR data set further includes masking, by the processor, an occlusion part of the first range view image corresponding to the second domain to be excluded from the machine learning appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 10 not eligible. Claim 12 related to wherein the LiDAR semantic segmentation model is further constructed by converting vertical coordinate values of the first LiDAR data set according to vertical coordinates of the second domain appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 12 not eligible. Claim 13 related to , wherein the LiDAR semantic segmentation model is further constructed by converting the first LiDAR data set into a first range view image having a first horizontal resolution (a number of pixels) and a first vertical resolution (a number of channels), and converting the first range view image into a second range view image having a horizontal resolution and a vertical resolution corresponding to the second domain appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 13 not eligible. Claim 14 related to wherein the LiDAR semantic segmentation model is further constructed by obtaining the second range view image by mapping the first range view image to a range view image frame of a second horizontal resolution (a number of pixels) appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 14 not eligible. Claim 15 related to wherein the first semantic segmentation model includes a deep-learning network having n (integer) encoder layers, wherein the second horizontal resolution may be a number that is directly above a number obtained by dividing 3600 by a horizontal scan resolution (angle) of the second domain among multiples of 2 to a power of n appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 15 not eligible. Claim 16 related to , wherein in the obtaining of the second range view image, and in response to the first horizontal resolution being greater than the second horizontal resolution and a plurality of pixels of the first range view image are mapped to one pixel of the second range view image, a LiDAR point having a smaller distance coordinate value among LiDAR points corresponding to the plurality of pixels is selected for mapping appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 16 not eligible. Claim 17 related to wherein the LiDAR semantic segmentation model is further constructed by obtaining the second range view image by converting the first range view image into a range view image of a second vertical resolution (a number of channels) appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 17 not eligible. Claim 18 related to wherein the converting of the first range view image includes converting the first range view image into a "pitch-density function" domain, dividing a pitch axis into equal parts by the second vertical resolution (the number of channels), and obtaining the second range view image from a density function value corresponding to the equal parts appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 18 not eligible. Claim 19 related to wherein a density function value of an equal part that does not have a corresponding density function value among the equal parts is determined to be 0 (zero) appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim19 not eligible. Claim 20 related to wherein the LiDAR semantic segmentation model is further constructed by masking an occlusion part of the first range view image corresponding to the second domain to be excluded from the machine learning appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 20 not eligible. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA Claim (s) 1, 11, 2, 3, 4, 6, 7, 10, 12, 13, 14, 16, 17, 20 are rejected under 35 U.S.C. 102 ( a) (1 ) as being anticipated by KOTOV, CN 114026458 A, DATE PUBLISHED: 2022-02-08, CPC G06T 7/62 . Regarding claim 1: KOTOV described a method for constructing a LiDAR semantic segmentation model through two-step domain adaptation, the method comprising: converting, by a processor, a first LiDAR data set of a first domain to obtain a second LiDAR data set of a second domain (page 23-24, two-stage Lidar data processing by enhancing a static object classifier by adding material size to the data) ; performing, by the processor, a machine learning with the second LiDAR data set as training data to obtain a first semantic segmentation model of an artificial intelligence model (page 24, learning using artificial neural network algorithm) ; and performing, by the processor, a feature domain adaptation for the first semantic segmentation model utilizing target data to obtain a second semantic segmentation model (page 23-24, enhancing static object) . Regarding claim 11: KOTOV described a LiDAR-based object perception apparatus, comprising: a LiDAR sensor that obtains cloud points for a surrounding environment; a computer-readable recording medium storing a computer program, which when executed causes a segmentation on the cloud points according to a LiDAR semantic segmentation model; and a processor executing the computer program (page 29, using computer on LIDAR) , wherein the LiDAR semantic segmentation model is constructed by obtaining a second LiDAR data set of a second domain by converting a first LiDAR data set of a first domain (page 23-24, two-stage Lidar data processing by enhancing a static object classifier by adding material size to the data) , obtaining a first semantic segmentation model of an artificial intelligence model by performing a machine learning with the second LiDAR data set as training data (page 24, learning using artificial neural network algorithm) , and obtaining a second semantic segmentation model by performing a feature-domain adaptation for the first semantic segmentation model utilizing target data page 23-24, enhancing static object) . Regarding claim 2, KOTOV further described wherein the converting of the first LiDAR data set includes converting, by the processor, vertical coordinate values of the first LiDAR data set according to vertical coordinates of the second domain (page 12, LIDAr having vertical orientation conversion detector) . Regarding claim 3, KOTOV further described the converting of the first LiDAR data set includes converting, by the processor, the first LiDAR data set into a first range view image having a first horizontal resolution (a number of pixels) (page 31, Pixel resolution) and a first vertical resolution (a number of channels) (page 12, LIDAr having vertical orientation conversion detector) , and converting, by the processor, the first range view image into a second range view image having a horizontal resolution and a vertical resolution corresponding to the second domain (page 23-24, two-stage Lidar data processing by enhancing a static object classifier by adding material size to the data) . Regarding claim 4, KOTOV further described the converting of the first LiDAR data set further includes obtaining, by the processor, the second range view image by mapping, by the processor, the first range view image to a range view image frame of a second horizontal resolution (a number of pixels) (fig. 2a, 2b) . Regarding claim 6, KOTOV further described obtaining of the second range view image, in response to the first horizontal resolution being greater than the second horizontal resolution and a plurality of pixels of the first range view image are mapped to one pixel of the second range view image, includes performing, by the processor, a mapping by selecting, by the processor, a LiDAR point having a smaller distance coordinate value among LiDAR points corresponding to the plurality of pixels (page 18, can increase to any limit strain) . Regarding claim 7, KOTOV further described includes obtaining, by the processor, the second range view image by converting, by the processor, the first range view image into a range view image of a second vertical resolution (a number of channels) (fig. 2a, 2b) . Regarding claim 10, KOTOV further described wherein the converting of the first LiDAR data set further includes masking, by the processor, an occlusion part of the first range view image corresponding to the second domain to be excluded from the machine learning (fig. 2a, 2b) . Regarding claim 12, KOTOV further described wherein the converting of the first LiDAR data set includes converting, by the processor, vertical coordinate values of the first LiDAR data set according to vertical coordinates of the second domain (page 12, LIDAr having vertical orientation conversion detector) . Regarding claim 13, KOTOV further described the converting of the first LiDAR data set includes converting, by the processor, the first LiDAR data set into a first range view image having a first horizontal resolution (a number of pixels) (page 31, Pixel resolution) and a first vertical resolution (a number of channels) (page 12, LIDAr having vertical orientation conversion detector) , and converting, by the processor, the first range view image into a second range view image having a horizontal resolution and a vertical resolution corresponding to the second domain (page 23-24, two-stage Lidar data processing by enhancing a static object classifier by adding material size to the data) . Regarding claim 14, KOTOV further described the converting of the first LiDAR data set further includes obtaining, by the processor, the second range view image by mapping, by the processor, the first range view image to a range view image frame of a second horizontal resolution (a number of pixels) (fig. 2a, 2b) . Regarding claim 16, KOTOV further described obtaining of the second range view image, in response to the first horizontal resolution being greater than the second horizontal resolution and a plurality of pixels of the first range view image are mapped to one pixel of the second range view image, includes performing, by the processor, a mapping by selecting, by the processor, a LiDAR point having a smaller distance coordinate value among LiDAR points corresponding to the plurality of pixels (page 18, can increase to any limit strain) . Regarding claim 17, KOTOV further described includes obtaining, by the processor, the second range view image by converting, by the processor, the first range view image into a range view image of a second vertical resolution (a number of channels) (fig. 2a, 2b) . Regarding claim 20, KOTOV further described wherein the converting of the first LiDAR data set further includes masking, by the processor, an occlusion part of the first range view image corresponding to the second domain to be excluded from the machine learning (fig. 2a, 2b) . Contact information 5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tung Lau whose telephone number is (571)272-2274, email is Tungs.lau@uspto.gov. The examiner can normally be reached on Tuesday-Friday 7:00 AM-5:00 PM EST. 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, TURNER SHELBY, can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll- free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272- 1000. /TUNG S LAU/Primary Examiner, Art Unit 2857 Technology Center 2800 March 26, 2026 Application/Control Number: 18/523,163 Page 2 Art Unit: 2857 Application/Control Number: 18/523,163 Page 3 Art Unit: 2857 Application/Control Number: 18/523,163 Page 4 Art Unit: 2857 Application/Control Number: 18/523,163 Page 5 Art Unit: 2857 Application/Control Number: 18/523,163 Page 6 Art Unit: 2857 Application/Control Number: 18/523,163 Page 7 Art Unit: 2857 Application/Control Number: 18/523,163 Page 8 Art Unit: 2857 Application/Control Number: 18/523,163 Page 9 Art Unit: 2857 Application/Control Number: 18/523,163 Page 10 Art Unit: 2857 Application/Control Number: 18/523,163 Page 11 Art Unit: 2857 Application/Control Number: 18/523,163 Page 12 Art Unit: 2857 Application/Control Number: 18/523,163 Page 13 Art Unit: 2857 Application/Control Number: 18/523,163 Page 14 Art Unit: 2857
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