CTNF 18/727,260 CTNF 100770 DETAILED ACTION 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. Information Disclosure Statement All Information Disclosure Statements filed on or before 03/24/2026 have been considered by examiner. Specification The use of the terms: [Paragraph 2] Apple iPad Pro 2020 [Paragraph 2] Velodyne Velabit [Paragraph 2] Intel RealSense LiDAR camera L515 , which are trade names or a mark used in commerce, has been noted in this application. The terms should be accompanied by the generic terminology; furthermore the terms should be capitalized wherever they appear or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the terms. Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks. Claim Rejections - 35 USC § 103 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 (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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1, 6-10, 16-18, 20, and 36-45 are rejected under 35 U.S.C. 103 as being unpatentable over Iguchi (WO 2020189709 A1) in view of Jin (CN 112991473 A) . With respect to claim 1 , Iguchi teaches a method of decoding point cloud data (“The three-dimensional data decoding method according to one aspect of the present disclosure is a three-dimensional data decoding method using a first decoding method and a second decoding method different from the first decoding method, and is a point group data from a bit stream.” Page 4 paragraph 2), comprising: decoding a first version of a point cloud (“The three-dimensional data decoding method according to one aspect of the present disclosure is a three-dimensional data decoding method using a first decoding method and a second decoding method different from the first decoding method, and is a point group data from a bit stream.” Page 4 paragraph 2) ; obtaining a set of pointwise features for said first version of said point cloud (“The coded attribute information in which the plurality of attribute information of the plurality of three-dimensional points included in is encoded and the first quantization parameter are acquired, and the plurality of values of the first quantization parameter and the first scale value are acquired.” Page 4 paragraph 2) ; Iguchi does not teach obtaining refinement information for said first version of said point cloud from said set of pointwise features based on a point-based neural network; and obtaining a second version of said point cloud, based on said refinement information and said first version of said point cloud. Jin teaches obtaining refinement information for said first version of said point cloud from said set of pointwise features based on a point-based neural network (“the second step, selecting at least two point cloud model from the target category, and inputting the geometric feature extraction module of the neural network in parallel; the neural network automatically extracts several hidden codes corresponding to the selected point cloud model number by using the data enhancement module and the encoder module based on the picture idea;” page 8 paragraph 1) ; and obtaining a second version of said point cloud, based on said refinement information and said first version of said point cloud (“the fourth step, the hidden code after interpolation continuously as the input of the decoder, obtaining the output generating point cloud model, if the first step selects a target category, the output still belongs to the target category, and at the same time comprises the second step of the selected part of the model, if the first step selected more than one target category; then outputting the intermediate state of a plurality of target categories, such as a certain special category between the chair and the table, and comprising the geometric features of the table and the chair.” Page 8 paragraph 3) . Jin is analogous art in the same field of endeavor as the claimed invention. Jin is directed to coding point cloud data (“A neural network coding and decoding method based on cubic template, comprising: determining the needed data set as input point cloud data; the data set is point cloud data of single type or multiple types; the point cloud data is matrix form;” page 2 Contents of the Invention paragraph 4) . A person of ordinary skill in the art would have found it obvious to combine the systems of Iguchi and Jin by utilizing the reconstruction cloud output of Jin in response to the encoded output of Iguchi used within Iguchi’s encoding and decoding scheme, with the expectation that doing so would lead to a reduction in information loss and enable the ability to generate real point cloud format data ( “Compared with the existing technology, the invention is directly based on the real sampling mechanism of point cloud data, which can effectively encode the point cloud format data, and decoding the cloud point close to the data before coding from the hidden code, reducing information loss; Therefore, the coding decoder structure of the invention can also be used for constructing the classical deep learning network-network and so on, which provides the possibility for the real point cloud format data generation.” Page 4 The beneficial effects of the present invention ). With respect to claim 6 , Iguchi and Jin teach the method of claim 1. Iguchi teaches it further comprising: decoding a set of point cloud data, wherein said first version of said point cloud is decoded by de-quantizing said set of point cloud data (“The three-dimensional data decoding method according to one aspect of the present disclosure is a three-dimensional data decoding method using a first decoding method and a second decoding method different from the first decoding method, and is a point group data from a bit stream. The coded attribute information in which the plurality of attribute information of the plurality of three-dimensional points included in is encoded and the first quantization parameter are acquired, and the plurality of values of the first quantization parameter and the first scale value are acquired.” Page 4 paragraph 2) . With respect to claim 7 , Iguchi and Jin teach the method of claim 1. Jin further teaches wherein said set of features are constant (“obtaining point-by-point local geometric features of point cloud data in matrix form; rotating the local geometric feature and the input point cloud data in a uniform angle to realize data enhancement; coding the point cloud data with different lengths after being enhanced into the hidden coding vector with uniform size; decoding the hidden coding vector with uniform size based on the cube template into the output point cloud close to the input point cloud.” Page 2 Content of the Invention lines 7-13) . With respect to claim 8 , Iguchi and Jin teach the method of claim 1. Jin further teaches wherein said obtaining refinement information comprises: converting each feature in said set of pointwise feature to one or more 3D points (“decoding the hidden coding vector with uniform size based on the cube template into the output point cloud close to the input point cloud.” Page 2 Content of the Invention lines 12-13) . With respect to claim 9 , Iguchi and Jin teach the method of claim 8. Jin teaches it further comprising: removing a point from said one or more 3D points responsive to a distance of said point from the origin (“The Euclidean distance limit can ensure that the noise point which is far away from the far distance is excluded from the near point of the low area point of the inquiry density” page 5 lines 3-5) . With respect to claim 10 , Iguchi and Jin teach the method of claim 9, Jin further teaches wherein said distance is adaptive to a quantization step size used in dequantizing (“further, the point cloud point-by-point adjacent index is spliced as a neighbour index matrix, specifically is composed of a point cloud data in all points of the neighbour index set to the quantization split joint, dimension size is N * k, N is the number of point cloud point, k is the preset k neighbour parameter value.” Page 3 lines 12-14) . With respect to claim 16 , Iguchi teaches a method of encoding point cloud data (“As described above, the three-dimensional data encoding device 400 extracts and extracts the three-dimensional data 412 (second three-dimensional data) whose feature amount is equal to or larger than the threshold value from the input three-dimensional data 411 (first three-dimensional data). Encoded three-dimensional data 414 (first encoded three-dimensional data) is generated by encoding the three-dimensional data 412.” Page 22 paragraph 8) , comprising: encoding a first version of a point cloud (“As described above, the three-dimensional data encoding device 400 extracts and extracts the three-dimensional data 412 (second three-dimensional data) whose feature amount is equal to or larger than the threshold value from the input three-dimensional data 411 (first three-dimensional data). Encoded three-dimensional data 414 (first encoded three-dimensional data) is generated by encoding the three-dimensional data 412.” Page 22 paragraph 8) ; reconstructing a second version of said point cloud (“As described above, the three-dimensional data encoding device 400 extracts and extracts the three-dimensional data 412 (second three-dimensional data) whose feature amount is equal to or larger than the threshold value from the input three-dimensional data 411 (first three-dimensional data). Encoded three-dimensional data 414 (first encoded three-dimensional data) is generated by encoding the three-dimensional data 412.” Page 22 paragraph 8) ; Iguchi does not teach obtaining refinement information based on said second version of said point cloud and said point cloud; obtaining a set of pointwise features for said second version of said point cloud from said refinement information using a point-based neural network architecture; and encoding said pointwise feature set. Jin teaches obtaining refinement information based on said second version of said point cloud and said point cloud (“the second step, selecting at least two point cloud model from the target category, and inputting the geometric feature extraction module of the neural network in parallel; the neural network automatically extracts several hidden codes corresponding to the selected point cloud model number by using the data enhancement module and the encoder module based on the picture idea;” page 8 paragraph 1) ; obtaining a set of pointwise features for said second version of said point cloud from said refinement information using a point-based neural network architecture (“the second step, selecting at least two point cloud model from the target category, and inputting the geometric feature extraction module of the neural network in parallel; the neural network automatically extracts several hidden codes corresponding to the selected point cloud model number by using the data enhancement module and the encoder module based on the picture idea;” page 8 paragraph 1) ; and encoding said pointwise feature set (“the second step, selecting at least two point cloud model from the target category, and inputting the geometric feature extraction module of the neural network in parallel; the neural network automatically extracts several hidden codes corresponding to the selected point cloud model number by using the data enhancement module and the encoder module based on the picture idea;” page 8 paragraph 1) . Jin is analogous art in the same field of endeavor as the claimed invention. Jin is directed to coding point cloud data (“A neural network coding and decoding method based on cubic template, comprising: determining the needed data set as input point cloud data; the data set is point cloud data of single type or multiple types; the point cloud data is matrix form;” page 2 Contents of the Invention paragraph 4) . A person of ordinary skill in the art would have found it obvious to combine the systems of Iguchi and Jin by utilizing the reconstruction cloud output of Jin in response to the encoded output of Iguchi used within Iguchi’s encoding and decoding scheme, with the expectation that doing so would lead to a reduction in information loss and enable the ability to generate real point cloud format data ( “Compared with the existing technology, the invention is directly based on the real sampling mechanism of point cloud data, which can effectively encode the point cloud format data, and decoding the cloud point close to the data before coding from the hidden code, reducing information loss; Therefore, the coding decoder structure of the invention can also be used for constructing the classical deep learning network-network and so on, which provides the possibility for the real point cloud format data generation.” Page 4 The beneficial effects of the present invention ). With respect to claim 17 , Iguchi and Jin teach the method of claim 16. Jin further teaches wherein said refinement information corresponds to a residual component (“the first step, determining one or several target category, the category should exist in the training data set in the neural network training process;” page 7 last paragraph and “the second step, selecting at least two point cloud model from the target category, and inputting the geometric feature extraction module of the neural network in parallel; the neural network automatically extracts several hidden codes corresponding to the selected point cloud model number by using the data enhancement module and the encoder module based on the picture idea;” page 8 paragraph 1) . With respect to claim 18 , Iguchi and Jin teach the method of claim 16. Iguchi further teaches wherein said reconstructing a second version of said point cloud comprises: dequantizing decoded first version of said point cloud to form said second version of said point cloud (“The three-dimensional data decoding method according to one aspect of the present disclosure is a three-dimensional data decoding method using a first decoding method and a second decoding method different from the first decoding method, and is a point group data from a bit stream. The coded attribute information in which the plurality of attribute information of the plurality of three-dimensional points included in is encoded and the first quantization parameter are acquired, and the plurality of values of the first quantization parameter and the first scale value are acquired.” Page 4 paragraph 2) . With respect to claim 20 , Iguchi and Jin teach the method of claim 16. Jin further teaches wherein said obtaining refinement information comprises, for a point in said second version of said point cloud: obtaining one or more nearest neighbors in said first version of said point cloud (“the second step, selecting at least two point cloud model from the target category, and inputting the geometric feature extraction module of the neural network in parallel; the neural network automatically extracts several hidden codes corresponding to the selected point cloud model number by using the data enhancement module and the encoder module based on the picture idea;” page 8 paragraph 7 and “the point cloud point-by-point neighbour index is combined k neighbour method and Euclidean distance method; the neighbour point index set of each point of the point cloud model is extracted; k neighbour can ensure the point of the normal density area in the point cloud to extract the index set of equal number k; The Euclidean distance limit can ensure that the noise point which is far away from the far distance is excluded from the near point of the low area point of the inquiry density.” Page 5 paragraph 1) ; and obtaining a respective difference between 3D coordinates of said point and each point of said one or more nearest neighbors (“the point cloud point-by-point neighbour index is combined k neighbour method and Euclidean distance method; the neighbour point index set of each point of the point cloud model is extracted; k neighbour can ensure the point of the normal density area in the point cloud to extract the index set of equal number k; The Euclidean distance limit can ensure that the noise point which is far away from the far distance is excluded from the near point of the low area point of the inquiry density.” Page 5 paragraph 1) . With respect to claim 36 , Iguchi and Jin render obvious all limitations in consideration of claim 1 due to claim 36 being directed towards an apparatus capable of preforming the method of claim 1 and the substantial similarities between claim 36 and claim 1. Iguchi additionally teaches an apparatus, comprising one or more processors and at least one memory coupled to said one or more processors (“Further, the three-dimensional data coding device according to one aspect of the present disclosure is a three-dimensional data coding device that uses a first coding method and a second coding method different from the first coding method. The processor includes a processor and a memory…” page 5 lines 10-12) . With respect to claim 37 , Iguchi and Jin teach the apparatus of claim 36, and in consideration of claim 6, teach all limitations due to claim 37 being directed towards the apparatus that preforms the method of claim 6 and the substantial similarities between claim 37 and claim 6. Iguchi additional teaches the use of processors (“Further, the three-dimensional data coding device according to one aspect of the present disclosure is a three-dimensional data coding device that uses a first coding method and a second coding method different from the first coding method. The processor includes a processor and a memory…” page 5 lines 10-12) . With respect to claim 38 , Iguchi and Jin teach the apparatus of claim 36, and all further limitations in consideration of claim 7, due to claim 38 being directed towards the apparatus that preforms the method of claim 7 and the substantial similarities between claim 38 and claim 7. With respect to claim 39 , Iguchi and Jin teach the apparatus of claim 36, and all further limitations in consideration of claim 8, due to claim 39 being directed towards the apparatus that preforms the method of claim 8 and the substantial similarities between claim 39 and claim 8. Iguchi additional teaches the use of processors (“Further, the three-dimensional data coding device according to one aspect of the present disclosure is a three-dimensional data coding device that uses a first coding method and a second coding method different from the first coding method. The processor includes a processor and a memory…” page 5 lines 10-12) . With respect to claim 40 , Iguchi and Jin teach the apparatus of claim 39, and all further limitations in consideration of claim 9, due to claim 40 being directed towards the apparatus that preforms the method of claim 9 and the substantial similarities between claim 40 and claim 9. Iguchi additional teaches the use of processors (“Further, the three-dimensional data coding device according to one aspect of the present disclosure is a three-dimensional data coding device that uses a first coding method and a second coding method different from the first coding method. The processor includes a processor and a memory…” page 5 lines 10-12) . With respect to claim 41 , Iguchi and Jin teach the apparatus of claim 40, and all further limitations in consideration of claim 10, due to claim 41 being directed towards the apparatus that preforms the method of claim 10 and the substantial similarities between claim 41 and claim 10. With respect to claim 42 , Iguchi and Jin teach all claim limitations in consideration of claim 16, due to claim 42 being directed towards the apparatus that preforms the method of claim 16 and the substantial similarities between claim 42 and claim 16. Iguchi additionally teaches an apparatus, comprising one or more processors and at least one memory coupled to said one or more processors (“Further, the three-dimensional data coding device according to one aspect of the present disclosure is a three-dimensional data coding device that uses a first coding method and a second coding method different from the first coding method. The processor includes a processor and a memory…” page 5 lines 10-12) . With respect to claim 43 , Iguchi and Jin teach the apparatus of claim 42, and all further limitations in consideration of claim 17, due to claim 43 being directed towards the apparatus that preforms the method of claim 17 and the substantial similarities between claim 43 and claim 17. With respect to claim 44 , Iguchi and Jin teach the apparatus of claim 42, and all further limitations in consideration of claim 18, due to claim 44 being directed towards the apparatus that preforms the method of claim 18 and the substantial similarities between claim 44 and claim 18. Iguchi additional teaches the use of processors (“Further, the three-dimensional data coding device according to one aspect of the present disclosure is a three-dimensional data coding device that uses a first coding method and a second coding method different from the first coding method. The processor includes a processor and a memory…” page 5 lines 10-12) . With respect to claim 45 , Iguchi and Jin teach the apparatus of claim 42, and all further limitations in consideration of claim 20, due to claim 45 being directed towards the apparatus that preforms the method of claim 20 and the substantial similarities between claim 45 and claim 20. Iguchi additional teaches the use of processors (“Further, the three-dimensional data coding device according to one aspect of the present disclosure is a three-dimensional data coding device that uses a first coding method and a second coding method different from the first coding method. The processor includes a processor and a memory…” page 5 lines 10-12) . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA C WILLIAMS whose telephone number is (571)272-7074. The examiner can normally be reached M-F 7:30am - 4:00pm. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /REBECCA COLETTE WILLIAMS/Examiner, Art Unit 2677 /ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677 Application/Control Number: 18/727,260 Page 2 Art Unit: 2677 Application/Control Number: 18/727,260 Page 3 Art Unit: 2677 Application/Control Number: 18/727,260 Page 4 Art Unit: 2677 Application/Control Number: 18/727,260 Page 5 Art Unit: 2677 Application/Control Number: 18/727,260 Page 6 Art Unit: 2677 Application/Control Number: 18/727,260 Page 7 Art Unit: 2677 Application/Control Number: 18/727,260 Page 8 Art Unit: 2677 Application/Control Number: 18/727,260 Page 9 Art Unit: 2677 Application/Control Number: 18/727,260 Page 10 Art Unit: 2677 Application/Control Number: 18/727,260 Page 11 Art Unit: 2677 Application/Control Number: 18/727,260 Page 12 Art Unit: 2677 Application/Control Number: 18/727,260 Page 13 Art Unit: 2677
