DETAILED ACTION
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 (Korean Application KR-10-2022-0109131 filed August 20th, 2022).
Response to Arguments
Applicant amended claims 1 – 15 beyond formalities and 112 Rejections.
The pending claims are 1 – 15 [Page 11 lines 1 – 8].
Applicant provides Specification support for the various amendments made to the claims [Page 11 line 9 – Page 12 line 14].
Applicant amended the Title of the Invention, Abstract, and Specification to address Examiner’s Specification Objections [Page 12 lines 15 – 18].
Applicant amended the claims to address Examiner’s 112(b) Rejections [Page 12 line 19 – Page 13 line 9]. The Examiner reconsiders the 112(b) Rejections in view of the amended claims.
Applicant’s arguments with respect to claim(s) 1, 10 – 11, and 14 [Regarding Examiner’s 35 USC 102 Rejection] have been considered but 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.
First, the Applicant provides their summary of amended independent claim 1 [Page 13 lines 17 – 22].
Second, the Applicant discusses portions of Ram cited [Page 12 lines 23 – 28].
Third, the Applicant contends Ram does not teach features of amended independent claim 1 [Page 13 line 29 – Page 14 line 6] and similarly argues for the other amended independent claims [Page 14 lines 7 – 18]. However, Ram in Paragraphs 301 – 318 teaches road classification using thresholds and not performing motion compensation for the road.
Fourth, the Applicant contends the combination of Ram and Sugio do not render obvious features in amended claims 2, 12, and 15 [Page 14 line 19 – Page 15 line 15]. However, Sugio renders obvious roadways / roads imaged as static objects to be processed differently than dynamic objects. Thus, the roads is classified with thresholds (Ram Paragraphs 300 – 318) and is coded differently than dynamic objects as taught by at least Sugio. The arguments appears to be in view of amended independent claim 1 rather than the present amended claims.
Fifth, the Applicant contends the references cited do not render obvious features of amended claims 3 – 6, 8, and 13 [Page 15 line 16 – Page 16 line 19] arguing mainly in view of the amended independent claims. The Examiner disagrees for at least the reasons given in the Third and Fourth points above. In view of the amendments to the claims, the arguments are moot in view of the amended Rejection.
Sixth, the Applicant contends claim 9 is allowable for at least the reasons given for amended independent claim 1 [Page 16 line 20 – Page 17 line 2]. However, the Examiner disagrees for the reasons given at least in the Third point.
Sevent, the Applicant comments on amendments made to previously indicated allowable dependent claim 7 [Page 17 lines 3 – 22]. The Examiner reconsiders the claim in view of the amendments made.
While the Applicant’s points may be understood, the Examiner may respectfully disagrees; however in view of the amended claims, the Examiner may cite an additional / new reference against the claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 28th, 2026 was filed before the mailing date of the Final Rejection (this Office Action). The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
The information disclosure statement (IDS) submitted on May 20th, 2025 was filed before the mailing date of the First Action on the Merits (mailed April 7th, 2026). The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 – 6 and 8 – 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ramasubramonian, et al. (US PG PUB 2023/0345045 A1 referred to as “Ram” throughout where citations will come from the US PG PUB in lieu of all enabling US Provisional Applications), Sugio, et al (CA-3103196-A1 referred to as “Sugio” throughout) [First Cited in the Office Action mailed April 7th, 2026], and further in view of Ramasubramonian, et al. (US PG PUB 2023/0102401 A1 referred to as “Ram 401” throughout where citations will come from the US PG PUB in lieu of all enabling US Provisional Applications).
Regarding claim 1, see claim 10 which is the apparatus performing the steps of the claimed method.
Regarding claim 11, see claim 14 which is the apparatus performing the steps of the claimed method.
Regarding claim 12, see claim 15 which is the apparatus performing the steps of the claimed method.
Regarding claim 13, see claim 3 for the same / similar limitations and claim 11 from which claim 13 depends for decoder citations as further Ram 401 in Figure 3 renders obvious a decoder to group points and search for objects at least as decoding is the obvious inverse to the encoder of claim 3 to one of ordinary skill in the art.
Regarding claim 10, Ram teaches a point cloud coding / decoding system with considerations for compressing point cloud data of objects (e.g. in a roadway / driving application) and classification considerations to code the objects including using geometry data. Sugio teaches searching considerations for searching and additional processing of objects in the geometry point cloud data captured and encoded / decoded (e.g. searching for dynamic objects detected). Ram 401 teaches additional threshold / difference considerations in grouping points representing and object together.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Ram’s object detection and point cloud coding to include modifications to the geometry data and search capabilities of the geometry data with object considerations as taught by Sugio and grouping of points considerations taught by Ram 401. The combination teaches
a memory [Ram Figures 1 – 3 (see at least reference characters 106, 112, and 120) as well as Paragraphs 40 – 44 (storage devices and memory implementations)]; and
at least one processor connected to the memory [Ram Figures 1 – 3 (see at least reference characters 200 and 300) as well as Paragraphs 41 – 43 and 49 – 41 (processor based implementations of encoders / decoders)], the at least one processor configured to:
encode point cloud data [Ram Figures 1 and 2 (see at least reference character 200) as well as Paragraphs 38 – 42 (encoding point cloud data and use of memories) and 48 – 50 (processor / circuit implementations for point cloud encoders)]; and
generate a bitstream including the point cloud data and parameter information [Ram Figures 1 and 2 (see at least reference characters 200, 108, 110, 112, 114, and 122) as well as Paragraphs 42 – 50 (see at least system on chip, routers, switches, and network implementation of transmitters / interfaces to output encoded point cloud data) and 61 – 74 (encoding / decoding geometry data from the point cloud) and see the “wherein the parameter information includes …” limitation for additional citations],
wherein the at least one processor [See “at least one processor” limitation above for citations] is further configured to encode geometry of the point cloud data [Ram Figures 2 (see at least reference characters 200 and 216) and 6 - 9 (scanning and forming geometry data) as well as Paragraphs 58 and 61 – 68 (geometry based PCC encoding with geometry data based on object type)],
wherein the parameter information includes threshold information and object classification information [Ram Figures 6 – 9 and 14 (see object imaged) as well as Paragraphs 48 – 57 (e.g. Category 1 and 3 types of coding / partitions which also describes objects imaged), 144 – 151 (features of objects / collections of points) 166 – 170 and 175 – 180 (inter prediction information searched for objects as part of geometry information), 265 – 275 (searching encoded geometry data), 300 – 318 (classifying objects imaged and where the road is a static object and checking for points as within a delta threshold for the same laser ID as part of the object which renders obvious the claimed “parameter information” to one of ordinary skill in the art and to combine with the understandings of Sugio and the thresholds of Ram 401 Paragraph 148 (azimuth threshold as parameter information to group points or on a common characteristic (e.g. Sugio’s static or dynamic objects))); Sugio Figures 5 – 10 (objects and group of points / group of spaces geometry data), 46 – 52 and 103, 116 – 121 (searching algorithms going through geometry data) as well as Paragraphs 60 – 72 (classifying and processing static (e.g. road) and dynamic objects in combination with Paragraphs 105 – 110 (tagging / assigning classification values to objects)), 97 – 104 (spaces to search for objects as metadata information), 121 – 124, 129 – 134, 170 – 175, and 202 – 212 (classification searching points / geometry data including classifying data as static road object data), 416, 434 – 440 (point searching to generate predictors for objects), 464 – 470 (radius values used as a threshold to classify / assign attribute values to points) and 761 – 767 (search for points to join / use for prediction of object / group of space / points) where Sugio renders obvious objects as a collection of points],
wherein the object classification information is used to classify an object and a road [Ram Figures 6 – 9 and 14 (see object imaged) as well as Paragraphs 48 – 57 (e.g. Category 1 and 3 types of coding / partitions which also describes objects imaged), 144 – 151 (features of objects / collections of points and labelling / classifying objects) 166 – 170 and 175 – 180 (inter prediction information searched for objects as part of geometry information), 265 – 275 (searching encoded geometry data), 300 – 318 (classifying objects imaged and where the road is a static object and checking for points as within a delta threshold for the same laser ID as part of the object which renders obvious the claimed “parameter information” to one of ordinary skill in the art and to combine with the understandings of Sugio and the thresholds of Ram 401 Paragraph 148 (azimuth threshold as parameter information to group points or on a common characteristic (e.g. Sugio’s static or dynamic objects))); Sugio Figures 5 – 10 (objects and group of points / group of spaces geometry data), 46 – 52 and 103, 116 – 121 (searching algorithms going through geometry data) as well as Paragraphs 60 – 72 (classifying and processing static (e.g. road) and dynamic objects in combination with Paragraphs 105 – 110 (tagging / assigning classification values to objects rendering obvious the claimed “classification information”)), 97 – 104 (spaces to search for objects as metadata information), 121 – 124, 129 – 134, 170 – 175, and 202 – 212 (classification searching points / geometry data including classifying data as static road object data), 416, 434 – 440 (point searching to generate predictors for objects), 464 – 470 (radius values used as a threshold to classify / assign attribute values to points) and 761 – 767 (search for points to join / use for prediction of object / group of space / points) where Sugio renders obvious objects as a collection of points],
wherein the threshold information and the object classification information are used to determine points to which motion compensation is applied [See previous two limitations for citations of the “threshold information” and “object classification information” claimed and see the next limitation for citations of the determination of points “motion compensation is applied” to as claimed], and
wherein the motion compensation is applied to the determined points [Ram Figures 10, 12 – 14 as well as Paragraphs 34 – 36, 140 – 145, and 180 (motion compensation in various coordinate systems with selective use of predictors / pixels / points motion compensation is provided), 292, 323 – 325 and 335 – 339 (motion compensation NOT applied to the zero reference frame points (Ram Paragraph 292) of static objects including roads (Ram Paragraphs 300 – 318 or alternatively Sugio’s static or dynamic objects or Ram 401 Paragraphs 136 and 148)); Sugio Figures 10, 35 – 37 (see at least reference characters 1311 and 1408), 45 – 51, 103 and 116 – 121 as well as Paragraphs 60 – 72 (coding static (road) and dynamic objects differently in a second method (not the first)), 106 – 110 (tags for static / dynamic objects in the point cloud data), 122 – 132 (different encoding / decoding processes for dynamic / statis objects), 379 – 385 and 400 – 410 (inter prediction on spaces formed based on static / dynamic object classification in at least Paragraphs 122 – 132), and 761 – 767 (search for points to join / use for prediction of object / group of space / points)].
The motivation to combine Sugio with Ram is to combine features in the same / related field of invention of point cloud / group encoding techniques [Sugio Paragraphs 1 – 3] in order to improve coding / decoding efficiency [Sugio Paragraph 11 where the Examiner observes KSR Rationales (D) or (F) are also applicable].
The motivation to combine Ram 401 with Sugio and Ram is to combine features in the same /related field of invention of point cloud encoding / decoding [Ram 401 Paragraphs 2 – 3] in order to improve coding efficiency including the prediction portion of the encoder / decoder [Ram 401 Paragraphs 3 – 5 and 145 where the Examiner observes KSR Rationales (D) or (F) are also applicable].
This is the motivation to combine Ram, Sugio, and Ram 401 which will be used throughout the Rejection.
Regarding claim 14, Ram teaches a point cloud coding / decoding system with considerations for compressing point cloud data of objects (e.g. in a roadway / driving application) and classification considerations to code the objects including using geometry data. Sugio teaches searching considerations for searching and additional processing of objects in the geometry point cloud data captured and encoded / decoded (e.g. searching for dynamic objects detected). Ram 401 teaches additional threshold / difference considerations in grouping points representing and object together.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Ram’s object detection and point cloud coding to include modifications to the geometry data and search capabilities of the geometry data with object considerations as taught by Sugio and grouping of points considerations taught by Ram 401. The combination teaches
a memory [Ram Figures 1 – 3 (see at least reference characters 106, 112, and 120) as well as Paragraphs 40 – 44 (storage devices and memory implementations)]; and
at least one processor connected to the memory [Ram Figures 1 – 3 (see at least reference characters 200 and 300) as well as Paragraphs 41 – 43 and 49 – 41 (processor based implementations of encoders / decoders)], the at least one processor configured to:
obtain a bitstream including point cloud data and parameter information [Ram Figures 1 and 3 (see at least reference characters 300, 108, 110, 112, 114, and 122) as well as Paragraphs 42 – 50 (see at least system on chip, routers, switches, and network implementation of receiver / interfaces to input encoded point cloud data to decode), and 69 – 74 (decoding geometry data from the point cloud) and see the “wherein the parameter information includes …” limitation for additional citations]; and
decode the point cloud data [Ram Figures 1 and 3 (see at least reference character 300) as well as Paragraphs 38 – 42 (decoding point cloud data and use of memories), 48 – 50 (processor / circuit implementations for point cloud decoders), and 69 – 74 (decoding geometry data from the point cloud)],
wherein the at least one processor [See “at least one processor” limitation above for citations] is further configured to decode geometry of the point cloud data,
wherein the parameter information includes threshold information and object classification information [See claim 10 for the same / similar limitation for citations],
wherein the object classification information is used to classify an object and a road [See claim 10 for the same / similar limitation for citations],
wherein the threshold information and the object classification information are used to determine points to which motion compensation is applied [See claim 10 for the same / similar limitation for citations], and
wherein the motion compensation is applied to the determined points [See claim 10 for the same / similar limitation for citations].
The motivation to combine Sugio with Ram is to combine features in the same / related field of invention of point cloud / group encoding techniques [Sugio Paragraphs 1 – 3] in order to improve coding / decoding efficiency [Sugio Paragraph 11 where the Examiner observes KSR Rationales (D) or (F) are also applicable].
The motivation to combine Ram 401 with Sugio and Ram is to combine features in the same /related field of invention of point cloud encoding / decoding [Ram 401 Paragraphs 2 – 3] in order to improve coding efficiency including the prediction portion of the encoder / decoder [Ram 401 Paragraphs 3 – 5 and 145 where the Examiner observes KSR Rationales (D) or (F) are also applicable].
This is the motivation to combine Ram, Sugio, and Ram 401 which will be used throughout the Rejection.
Regarding claim 2, Ram teaches a point cloud coding / decoding system with considerations for compressing point cloud data of objects (e.g. in a roadway / driving application) and classification considerations to code the objects including using geometry data. Sugio teaches searching considerations for searching and additional processing of objects in the geometry point cloud data captured and encoded / decoded (e.g. searching for dynamic objects detected). Ram 401 teaches additional threshold / difference considerations in grouping points representing and object together.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Ram’s object detection and point cloud coding to include modifications to the geometry data and search capabilities of the geometry data with object considerations as taught by Sugio and grouping of points considerations taught by Ram 401. The combination teaches
searching for the object based on the geometry [Ram Figures 2 (see at least reference characters 200 and 216) and 6 – 9 (scanning and forming geometry data) as well as Paragraphs 58 and 61 – 68 (geometry based PCC encoding with geometry data based on object type), 144 (labelling an object), 166 – 170 and 177 – 182 (inter prediction information searched as part of geometry information where points represent an object), and 265 – 275 (searching encoded geometry data); Sugio Figure 10 (objects and group of points / group of spaces geometry data), 47 – 50 and 103, 116 – 121 (searching algorithms going through geometry data) as well as Paragraphs 97 – 104 (spaces to search for objects as metadata information), 416, 434 – 440 (point searching to generate predictors for objects), and 761 – 767 (search for points to join / use for prediction of object / group of space / points)], and
wherein the object classification information is generated based on the object searched based on the geometry [See previous limitation for citations and additionally Ram Paragraphs 175 – 180 (searching point cloud data for objects) and 300 – 318 (road classification as a static object); Sugio Figures 5 – 8 and 46 – 52 as well as Paragraphs 60 – 72 (classifying and processing static (e.g. road) and dynamic objects in combination with Paragraphs 105 – 110 (tagging / assigning classification values to objects)), 121 – 124, 129 – 134, 170 – 175, and 202 – 212 (classification searching points / geometry data including classifying data as static road object data)].
See claim 1 for the motivation to combine Ram, Sugio, and Ram 401.
Regarding claim 3, Ram teaches a point cloud coding / decoding system with considerations for compressing point cloud data of objects (e.g. in a roadway / driving application) and classification considerations to code the objects including using geometry data. Sugio teaches searching considerations for searching and additional processing of objects in the geometry point cloud data captured and encoded / decoded (e.g. searching for dynamic objects detected). Ram 401 teaches additional threshold / difference considerations in grouping points representing and object together.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Ram’s object detection and point cloud coding to include modifications to the geometry data and search capabilities of the geometry data with object considerations as taught by Sugio and grouping of points considerations taught by Ram 401. The combination teaches
wherein the encoding of the geometry comprises transforming coordinates of points in the point cloud data from a Cartesian coordinate system to radius, azimuth, and laser ID [Ram Figures 2 and 7 – 8 (see at least reference character 202) as well as Paragraphs 64 – 67 and 81 (transforming coordinates into radius, azimuth, and height / laser ID domain), 274 – 276 and 281 – 284 (coordinates in radius, azimuth, and laser ID for predictive encoding)], wherein
the points are sorted based on a value of the laser ID [Ram Figures 5B (laser ID based on azimuth), 7 – 9, and 14 as well as Paragraphs 82 and 222 – 242 (sorting points based on laser ID / arranged points based on vertical heigh); Ram 401 Paragraphs 124 – 129 (ordering points for coding / prediction based on Laser ID) and 148 – 154 (grouping points based on LaserID)],
wherein the points for the value of the laser ID are clustered based on the radius and the azimuth [Ram Paragraphs 179 – 184 (radius used in inter prediction / grouping of points) and 222 – 242 (grouping / merging / clustering points with the same laser index / ID); Ram 401 Figures 6 – 8 as well as Paragraphs 135 – 142 (grouping points / clustering based on radius and azimuth being near / similar for the same laser ID) and 148 – 159 (grouping of points for the same Laser ID based on azimuth / radius and inferring values for same / small differences in radius or azimuth))], and
wherein the threshold information comprises at least one of a threshold for the azimuth or a threshold for the radius [Ram Figures 6 – 9 and 14 (see object imaged) as well as Paragraphs 48 – 57 (e.g. Category 1 and 3 types of coding / partitions which also describes objects imaged), 144 – 151 (features of objects / collections of points) 166 – 170 and 177 – 180 (inter prediction information searched as part of geometry information), 265 – 275 (searching encoded geometry data), 300 – 318 (classifying objects imaged on a road and checking for points as within a delta threshold for the same laser ID as part of the object – to combine with the understandings of Sugio and the thresholds of Ram 401 Paragraph 148 (azimuth threshold to group points or on a common characteristic (e.g. Sugio’s static or dynamic objects))); Sugio Figure 10 (objects and group of points / group of spaces geometry data), 47 – 51 and 103, 116 – 121 (searching algorithms going through geometry data) as well as Paragraphs 97 – 104 (spaces to search for objects as metadata information), 416, 434 – 440 (point searching to generate predictors for objects), 464 – 470 (radius values used as a threshold to classify / assign attribute values to points) and 761 – 767 (search for points to join / use for prediction of object / group of space / points) where Sugio renders obvious objects as a collection of points].
See claim 1 for the motivation to combine Ram, Sugio, and Ram 401.
Regarding claim 4, Ram teaches a point cloud coding / decoding system with considerations for compressing point cloud data of objects (e.g. in a roadway / driving application) and classification considerations to code the objects including using geometry data. Sugio teaches searching considerations for searching and additional processing of objects in the geometry point cloud data captured and encoded / decoded (e.g. searching for dynamic objects detected). Ram 401 teaches additional threshold / difference considerations in grouping points representing and object together.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Ram’s object detection and point cloud coding to include modifications to the geometry data and search capabilities of the geometry data with object considerations as taught by Sugio and grouping of points considerations taught by Ram 401. The combination teaches
wherein, based on at least one of:
a difference in the azimuth between a first point of the points for the laser ID and a second point of the points for the laser ID being less than the threshold for the azimuth [Ram Figures 8 and 12 as well as Paragraphs 142, 151, and 165 (differences in coordinate values), 180 – 182 (checking for near zero residual values for grouping), 222 – 242 (grouping points with near differences / small residuals), 281 – 285 and 324 – 337 (using threshold to group / determine points being similar to include); Ram 401 Figures 9 and 12 as well as Paragraphs 148 – 152 (grouping points with difference / threshold considerations of the azimuth for the same laser ID and likewise for the radius value)];
a difference in the radius between the first point of the points for the laser ID and the second point of the points for the laser ID being less than the threshold for the radius [Ram Figures 8 and 12 as well as Paragraphs 142, 151, and 165 (differences in coordinate values), 180 – 182 (checking for near zero residual values for grouping), 222 – 242 (grouping points with near differences / small residuals), 281 – 285 and 324 – 337 (using threshold to group / determine points being similar to include); Ram 401 Figures 9 and 12 as well as Paragraphs 148 – 152 and 156 – 159 (grouping points with difference / threshold considerations of the azimuth for the same laser ID and likewise for the radius value)], or
the difference in the azimuth between the first point of the points for the laser ID and the second point of the points for the laser ID being less than the threshold for the azimuth [Ram Figures 8 and 12 as well as Paragraphs 142, 151, and 165 (differences in coordinate values), 180 – 182 (checking for near zero residual values for grouping), 222 – 242 (grouping points with near differences / small residuals), 281 – 285 and 324 – 337 (using threshold to group / determine points being similar to include); Ram 401 Figures 9 and 12 as well as Paragraphs 148 – 152 (grouping points with difference / threshold considerations of the azimuth for the same laser ID and likewise for the radius value)]; and
the difference in the radius between the first point of the points for the laser ID and the second point of the points for the laser ID being less than the threshold for the radius [Ram Figures 8 and 12 as well as Paragraphs 142, 151, and 165 (differences in coordinate values), 180 – 182 (checking for near zero residual values for grouping), 222 – 242 (grouping points with near differences / small residuals), 281 – 285 and 324 – 337 (using threshold to group / determine points being similar to include); Ram 401 Figures 9 and 12 as well as Paragraphs 148 – 152 and 156 – 159 (grouping points with difference / threshold considerations of the azimuth for the same laser ID and likewise for the radius value)],
the first point and the second point are detected as a same object [See above limitations for the differences / thresholds used to group points and additionally Ram Figures 6 – 9 and 14 (see object imaged) as well as Paragraphs 48 – 57 (e.g. Category 1 and 3 types of coding / partitions which also describes objects imaged), 144 – 151 (features of objects / collections of points) 166 – 170 and 177 – 180 (inter prediction information searched as part of geometry information), 300 – 318 (classifying objects imaged on a road and checking for points as within a delta threshold for the same laser ID as part of the object – to combine with the understandings of Sugio and grouping of Ram 401 (see above limitations for citations)); Sugio Figure 10 (objects and group of points / group of spaces geometry data), 47 – 50 and 103, 116 – 121 (searching algorithms going through geometry data) as well as Paragraphs 97 – 104 (spaces to search for objects as metadata information), 416, 434 – 440 (point searching to generate predictors for objects), and 761 – 767 (search for points to join / use for prediction of object / group of space / points) where Sugio renders obvious objects as a collection of points].
See claim 1 for the motivation to combine Ram, Sugio, and Ram 401.
Regarding claim 5, Ram teaches a point cloud coding / decoding system with considerations for compressing point cloud data of objects (e.g. in a roadway / driving application) and classification considerations to code the objects including using geometry data. Sugio teaches searching considerations for searching and additional processing of objects in the geometry point cloud data captured and encoded / decoded (e.g. searching for dynamic objects detected). Ram 401 teaches additional threshold / difference considerations in grouping points representing and object together.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Ram’s object detection and point cloud coding to include modifications to the geometry data and search capabilities of the geometry data with object considerations as taught by Sugio and grouping of points considerations taught by Ram 401. The combination teaches
generating a predictive tree for points in the point cloud data [Ram Figure 6 (see at least reference character 600) as well as Paragraphs 78 – 82 (forming prediction tree) and 128 – 133 (signaling prediction tree information)];
generating predicted values for the geometry of the point cloud data based on the predictive tree [Ram Figure 6 (see at least reference character 600) as well as Paragraphs 78 – 82 (forming prediction tree) and 128 – 133 (signaling prediction tree information)];
generating residuals based on the predicted values [Ram Figure 6 (see at least reference character 600) as well as Paragraphs 78 – 82 (forming prediction tree and generating residual values based on prediction values / nodes predicted) and 120 – 129 (signaling prediction tree information and residual used for prediction tree generation)]; and
encoding the residuals [Ram Figure 2 and 6 (see at least reference characters 214, 226, and 600) as well as Paragraphs 61 – 68 (entropy encoding residuals in at least Paragraph 63), 78 – 82 (forming prediction tree and generating residual values based on prediction values / nodes predicted) and 120 – 129 (signaling prediction tree information and residual used for prediction tree generation)],
wherein the generating of the predictive tree comprises:
sorting the points [Ram Figures 5B (laser ID based on azimuth), 7 – 9, and 14 as well as Paragraphs 82, 191 – 199 (sorting in a depth first order), and 222 – 242 (sorting points based on laser ID / arranged points based on vertical heigh); Ram 401 Paragraphs 124 – 129 (ordering points for coding / prediction based on Laser ID) and 148 – 154 (grouping points based on LaserID)]; and
finding a neighbor node for a first point among the sorted points and adding the found neighbor node as a child node to a node of the first point [Ram Figures 4 – 9 (subfigures included and see Figure 4 for nearest neighbor considerations) as well as Paragraphs 58 – 64 (nearest neighbor / searching neighbors to include points and category of the point is assigned based on object type), 119 (condition for including neighbors in prediction), 128 – 134 and 148 – 151 (finding / predicting neighboring nodes)
Ram 401 Figures 4 and 8 as well as Paragraphs 46 – 50 (neighborhood point considerations with ordering of points considered in Paragraphs 58 – 61) and 187 (last point processed in grouping of points)].
See claim 1 for the motivation to combine Ram, Sugio, and Ram 401.
Regarding claim 6, Ram teaches a point cloud coding / decoding system with considerations for compressing point cloud data of objects (e.g. in a roadway / driving application) and classification considerations to code the objects including using geometry data. Sugio teaches searching considerations for searching and additional processing of objects in the geometry point cloud data captured and encoded / decoded (e.g. searching for dynamic objects detected). Ram 401 teaches additional threshold / difference considerations in grouping points representing and object together.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Ram’s object detection and point cloud coding to include modifications to the geometry data and search capabilities of the geometry data with object considerations as taught by Sugio and grouping of points considerations taught by Ram 401. The combination teaches
generating a predictive tree for points in a current frame containing the point cloud data [Ram Figure 6 (see at least reference character 600) as well as Paragraphs 78 – 82 (forming prediction tree) and 128 – 133 (signaling prediction tree information)];
generating predicted values for the geometry of the point cloud data from reference points in a reference frame for the current frame based on the predictive tree [Ram Figures 2 and 6 – 8 (see at least reference character 600) as well as Paragraphs 78 – 82 (forming prediction tree) and 122 – 133 (signaling prediction tree information with the prediction considering a current and a reference frame forming the prediction)];
generating residuals based on the predicted values [Ram Figure2 and 6 (see at least reference character 600) as well as Paragraphs 78 – 82 (forming prediction tree and generating residual values based on prediction values / nodes predicted) and 120 – 129 (signaling prediction tree information and residual used for prediction tree generation)]; and
encoding the residuals [Ram Figure 2 and 6 (see at least reference characters 214, 226, and 600) as well as Paragraphs 61 – 68 (entropy encoding residuals in at least Paragraph 63), 78 – 82 (forming prediction tree and generating residual values based on prediction values / nodes predicted) and 120 – 129 (signaling prediction tree information and residual used for prediction tree generation)],
wherein the reference points comprise:
a first point having an azimuth equal to an azimuth of a point in the current frame [Ram Figures 5 – 9 (subfigures included) as well as Paragraphs 34 – 35 (azimuth the same for comparing points between frames), 118 – 124 (points for comparison in prediction between frames), 128 – 132 and 135 – 140 (prediction techniques to find points with small azimuth differences within a threshold or same azimuth value for prediction)]; and
a second point having a laser ID identical to a laser ID of the first point and having an azimuth less than the azimuth of the first point [Ram Figures 5 – 9 (subfigures included) as well as Paragraphs 34 – 35 (azimuth the same for comparing points between frames), 118 – 124 (points for comparison in prediction between frames), 128 – 132 and 135 – 140 (prediction techniques to find points with small azimuth differences within a threshold or same azimuth value for prediction in which the closest azimuth value is used for prediction)].
See claim 1 for the motivation to combine Ram, Sugio, and Ram 401.
Regarding claim 8, Ram teaches a point cloud coding / decoding system with considerations for compressing point cloud data of objects (e.g. in a roadway / driving application) and classification considerations to code the objects including using geometry data. Sugio teaches searching considerations for searching and additional processing of objects in the geometry point cloud data captured and encoded / decoded (e.g. searching for dynamic objects detected). Ram 401 teaches additional threshold / difference considerations in grouping points representing and object together.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Ram’s object detection and point cloud coding to include modifications to the geometry data and search capabilities of the geometry data with object considerations as taught by Sugio and grouping of points considerations taught by Ram 401. The combination teaches
wherein, based on at least one of:
based on that a difference in the azimuth between a leading point and a last point among the points for the laser ID being less than the threshold for the azimuth [Ram Figures 8 and 12 as well as Paragraphs 142, 151, and 165 (differences in coordinate values), 180 – 182 (checking for near zero residual values for grouping), 222 – 242 (grouping points with near differences / small residuals), 281 – 285 and 324 – 337 (using threshold to group / determine points being similar to include); Ram 401 Figures 9 and 12 as well as Paragraphs 148 – 152 (grouping points with difference / threshold considerations of the azimuth for the same laser ID and likewise for the radius value in which the process is repeated up to a last point in Paragraph 187 rendering obvious the “last point” feature claimed)],
based on that a difference in the radius between the leading point and the last point among the points for the laser ID being less than the threshold for the radius [Ram Figures 8 and 12 as well as Paragraphs 142, 151, and 165 (differences in coordinate values), 180 – 182 (checking for near zero residual values for grouping), 222 – 242 (grouping points with near differences / small residuals), 281 – 285 and 324 – 337 (using threshold to group / determine points being similar to include); Ram 401 Figures 9 and 12 as well as Paragraphs 148 – 152 and 156 – 159 (grouping points with difference / threshold considerations of the azimuth for the same laser ID and likewise for the radius value in which the process is repeated up to a last point in Paragraph 187 rendering obvious the “last point” feature claimed)]; or
based on that the difference in the azimuth between the leading point and the last point among the points for the laser ID being less than the threshold for the azimuth [Ram Figures 8 and 12 as well as Paragraphs 142, 151, and 165 (differences in coordinate values), 180 – 182 (checking for near zero residual values for grouping), 222 – 242 (grouping points with near differences / small residuals), 281 – 285 and 324 – 337 (using threshold to group / determine points being similar to include); Ram 401 Figures 9 and 12 as well as Paragraphs 148 – 152 (grouping points with difference / threshold considerations of the azimuth for the same laser ID and likewise for the radius value in which the process is repeated up to a last point in Paragraph 187 rendering obvious the “last point” feature claimed)], and the difference in the radius between the leading point and the last point among the points for the laser ID being less than the threshold for the radius [Ram Figures 8 and 12 as well as Paragraphs 142, 151, and 165 (differences in coordinate values), 180 – 182 (checking for near zero residual values for grouping), 222 – 242 (grouping points with near differences / small residuals), 281 – 285 and 324 – 337 (using threshold to group / determine points being similar to include); Ram 401 Figures 9 and 12 as well as Paragraphs 148 – 152 and 156 – 159 (grouping points with difference / threshold considerations of the azimuth for the same laser ID and likewise for the radius value in which the process is repeated up to a last point in Paragraph 187 rendering obvious the “last point” feature claimed)],
the leading point and the last point are detected as a same object [Ram Figures 6 – 9 and 14 (see object imaged) as well as Paragraphs 48 – 57 (e.g. Category 1 and 3 types of coding / partitions which also describes objects imaged), 144 – 151 (features of objects / collections of points) 166 – 170 and 177 – 180 (inter prediction information searched as part of geometry information), 300 – 318 (classifying objects imaged on a road and checking for points as within a delta threshold for the same laser ID as part of the object – to combine with the understandings of Sugio and grouping of Ram 401 (see above limitations for citations)); Sugio Figure 10 (objects and group of points / group of spaces geometry data), 47 – 50 and 103, 116 – 121 (searching algorithms going through geometry data) as well as Paragraphs 97 – 104 (spaces to search for objects as metadata information), 416, 434 – 440 (point searching to generate predictors for objects to combine with Ram 401 grouping of points including last point of the group), and 761 – 767 (search for points to join / use for prediction of object / group of space / points) where Sugio renders obvious objects as a collection of points];
See claim 1 for the motivation to combine Ram, Sugio, and Ram 401.
Regarding claim 9, Ram teaches a point cloud coding / decoding system with considerations for compressing point cloud data of objects (e.g. in a roadway / driving application) and classification considerations to code the objects including using geometry data. Sugio teaches searching considerations for searching and additional processing of objects in the geometry point cloud data captured and encoded / decoded (e.g. searching for dynamic objects detected). Ram 401 teaches additional threshold / difference considerations in grouping points representing and object together.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Ram’s object detection and point cloud coding to include modifications to the geometry data and search capabilities of the geometry data with object considerations as taught by Sugio and grouping of points considerations taught by Ram 401. The combination teaches
wherein the parameter information comprises at least one of [See claim 1 for citations of the claimed “parameter information” and additionally Ram Figures 1 – 3 (encoder output and bitstream input) as well as Paragraphs 46 – 50 (encoder forms bitstream for the decoder to process); While other combinations of references may render more features as obvious, the Examiner in the sole interest of brevity / simplicity of the Rejection cites at least one item rendered obvious by Ram while more items may additionally be rendered obvious by Ram]:
a threshold for an azimuth [Ram Paragraphs 332 – 338 (se at least delAzim syntax element or difference thresholds in Paragraphs 128 and 337)];
a threshold for a radius [Ram Paragraphs 332 – 338 (se at least delRad syntax element or difference thresholds in Paragraphs 128 and 337)];
a flag related to a threshold for a laser ID;
information indicating a number of objects;
information indicating whether object search is performed between frames [Ram Figure 8 (see at least reference characters 800 and 808 for inter frame / inter prediction and intra prediction signaling)];
information indicating a bounding box of the object in a current frame [Ram Paragraphs 48 – 55 and 144 (bounding boxes for points / collection of points representing an object)];
information identifying the object [Ram Paragraphs 48 – 57 (e.g. Category 1 and 3 types of coding / partitions) and 144 – 151 (features of objects / collections of points)];
information indicating a type of the object [Ram Paragraphs 48 – 57 (e.g. Category 1 and 3 types of coding / partitions) and 144 – 151 (features of objects / collections of points)];
a motion vector for a dynamic object [Ram Paragraphs 48 – 55, 144, and 150 – 155 (features of objects / collections of points and motion vectors with the inter prediction mode used)];
information indicating a number of objects in a reference frame;
information indicating bounding boxes of the objects in the reference frame [Ram Paragraphs 48 – 55 and 144 (bounding boxes for points / collection of points representing an object)];
information identifying the objects in the reference frame [Ram Paragraphs 48 – 55 and 144 – 151 (features of objects / collections of points)]; or
information indicating types of the objects in the reference frame [Ram Paragraphs 48 – 57 (e.g. Category 1 and 3 types of coding / partitions) and 144 – 151 (features of objects / collections of points)].
See claim 1 for the motivation to combine Ram, Sugio, and Ram 401.
Regarding claim 15, Ram teaches a point cloud coding / decoding system with considerations for compressing point cloud data of objects (e.g. in a roadway / driving application) and classification considerations to code the objects including using geometry data. Sugio teaches searching considerations for searching and additional processing of objects in the geometry point cloud data captured and encoded / decoded (e.g. searching for dynamic objects detected).
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Ram’s object detection and point cloud coding to include modifications to the geometry data and search capabilities of the geometry data with object considerations as taught by Sugio. The combination teaches
search for the object based on the geometry [Ram Figures 2 (see at least reference characters 200 and 216) and 6 – 9 (scanning and forming geometry data) as well as Paragraphs 58 and 61 – 68 (geometry based PCC encoding with geometry data based on object type), 144 (labelling an object), 166 – 170 and 177 – 182 (inter prediction information searched as part of geometry information where points represent an object), and 265 – 275 (searching encoded geometry data); Sugio Figure 10 (objects and group of points / group of spaces geometry data), 47 – 50 and 103, 116 – 121 (searching algorithms going through geometry data) as well as Paragraphs 97 – 104 (spaces to search for objects as metadata information), 416, 434 – 440 (point searching to generate predictors for objects), and 761 – 767 (search for points to join / use for prediction of object / group of space / points)], and
wherein the object classification information is generated based on the object searched based on the geometry [See previous limitation for citations and additionally Ram Paragraphs 175 – 180 (searching point cloud data for objects) and 300 – 318 (road classification as a static object); Sugio Figures 5 – 8 and 46 – 52 as well as Paragraphs 60 – 72 (classifying and processing static (e.g. road) and dynamic objects in combination with Paragraphs 105 – 110 (tagging / assigning classification values to objects)), 121 – 124, 129 – 134, 170 – 175, and 202 – 212 (classification searching points / geometry data including classifying data as static road object data)].
See claim 14 for the motivation to combine Ram, Sugio, and Ram 401.
Allowable Subject Matter
Claim 7 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The prior art does not provide a fair teaching before the effective filing date of teaching all features of claim 7 where the classification of objects (dynamic and static) is based on a ratio of bounding boxes (the claimed “proportion”) detected between two frames where a threshold of the overlap determines the classification of the object. While Sugio Paragraph 122 discusses a ratio of objects, Sugio does not render obvious the bounding box overlap proportion / ratio as in the present invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Chen, et al. (US PG PUB 2020/0401135 A1 referred to as “Chen” throughout) in Paragraphs24 – 25 and 67 as using thresholds to detect roads.
Reference which does NOT qualify as prior art, but pertinent to the pending Application: Ramasubramonian, et al. (US PG PUB 2024/0233199 A1 referred to as “Ram 99” throughout).
Reference considered for ODP Rejection in view of future amendments made to the claims (in the interest of brevity the ODP Rejection is not made until amendments to the claims defining the invention is made): Hur (US Patent #12,273,557 B2 referred to as “Hur” throughout)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/TYLER W. SULLIVAN/Primary Examiner, Art Unit 2487
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