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 .
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.
Joint Inventors
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.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 09/30/2024 and 11/10/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). A certified copy of this document has been placed in the file wrapper. As such, the effective filing date of the instant application is considered 03/30/2022, coinciding with the filing date of the Japan application to which foreign priority was requested.
Claim Rejections - 35 USC § 101
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.
Claim 1 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim recites: “A data processing apparatus comprising: a segmentation unit configured to divide at least a part of distance image data including distance information to a measurement point in a measurement space into a plurality of first distance image segment data depending on the distance information; a converter configured to individually convert each of the plurality of first distance image segment data to first point cloud segment data being point cloud data including coordinate information of the measurement point in the measurement space; and a point cloud processing unit configured to individually perform processing on each of a plurality of the first point cloud segment data respectively obtained from the plurality of first distance image segment data in the converter.
These limitations, as drafted, are simple processes that, under their broadest reasonable interpretation, cover performance of the mind, but for the recitation of ‘a point cloud processing unit configured to individually perform processing’. That is, other than reciting the bolded limitations above, nothing in the claim elements preclude the steps from being performed in the mind or with pen and paper. For example, a human can, in their mind or with pen and paper, divide part of distance image data into a plurality of first distance image segment data depending on the distance information, convert segment data to point cloud segment data including coordinate information of the measurement point in the measurement space, and process the received data.
This judicial exception is not integrated into a practical application. The claim recites the additional steps of ‘a point cloud processing unit configured to individually perform processing’ at a high level of generality and merely link(s) the use of the abstract idea to a particular technological environment (see MPEP 2106.05(h)). In particular, the generation, population, display, storage, and further display of data described in the steps above are merely automated determination and data processing steps, implemented without any meaningful limitations to the performance of the abstract idea, and acting as a generic computer operating in its ordinary capacity. The inclusion of the in-vehicle computing device and in-vehicle computing device storage act as no more than mere instructions to apply the exception using a computer.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element(s) of ‘a point cloud processing unit configured to individually perform processing’ is/are no more than mere generic linking of the abstract idea to a technological environment, which cannot provide an inventive concept. Thus, the limitations do not provide an inventive concept, and the claim contains ineligible subject matter.
Claims 2-31 recite limitations that are no more that the abstract idea recited in claim 1.
Regarding claim 2: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 3: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 4: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 5: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 6: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 7: Rejected using the same rationale as claim 1.
Regarding claim 8: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 9: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 10: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 11: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 12: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 13: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 14: The claim recites a further limitation of the segment processing, which is a mental
process.
Regarding claim 15: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 16: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 17: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 18: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 20: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 21: Rejected using the same rationale as claims 1 and 7, however further directed to “a non-transitory computer-readable recording medium storing a program”, which is no more than mere generic linking of the abstract idea to a technological environment, which cannot provide an inventive concept.
Regarding claim 22: Rejected using the same rationale as claim 21
Regarding claim 23: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 24: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 25: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 26: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 27: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 28: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 29: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 30: The claim recites a further limitation of the segment processing, which is a mental process.
Regarding claim 31: The claim recites a further limitation of the segment processing, which is a mental process.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-31 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kao et al. (US20210150726, referred to as Kao).
Regarding claim 1: Kao discloses: A data processing apparatus comprising: a segmentation unit configured to divide at least a part of distance image data including distance information to a measurement point in a measurement space into a plurality of first distance image segment data depending on the distance information; a converter configured to individually convert each of the plurality of first distance image segment data to first point cloud segment data being point cloud data including coordinate information of the measurement point in the measurement space; and a point cloud processing unit configured to individually perform processing on each of a plurality of the first point cloud segment data respectively obtained from the plurality of first distance image segment data in the converter. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm. [0084] one method of determining the 3D point cloud data corresponding to the depth image based on the depth image is to convert depth information and 2D image coordinates of the depth image from an image coordinate system to a world coordinate system. The 3D point cloud data may describe a 3D structural feature of an object, that is, a 3D geometric feature in a 3D space, and each 3D point converted from a depth image back projection into a 3D space may correspond to each pixel of an original depth image. [0188] when the 3D detection result includes a 3D segmentation result and when the target image includes an object of an incomplete shape, an extraction result of an object included in a scene may be acquired based on the 3D point cloud data. [0189] In other words, the image processing method according to an example may complete 3D point cloud data corresponding to the object of the incomplete shape based on the object of the incomplete shape, may acquire the completed 3D point cloud data, and may acquire the extraction result of the object included in the scene based on the completed 3D point cloud data.)
Regarding claim 2: Kao discloses: The data processing apparatus according to claim 1,
Kao further discloses: wherein the segmentation unit divides first partial distance image data including the distance information related to a first object in the measurement space in the distance image data into the plurality of first distance image segment data. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm)
Regarding claim 3: Kao discloses: The data processing apparatus according to claim 2,
Kao further discloses: wherein the segmentation unit divides second partial distance image data including the distance information related to a second object in the measurement space in the distance image data into a plurality of second distance image segment data depending on the distance information, andthe converter individually converts each of the plurality of second distance image segment data to second point cloud segment data being the point cloud data including the coordinate information of the measurement point in the measurement space. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm [0084] one method of determining the 3D point cloud data corresponding to the depth image based on the depth image is to convert depth information and 2D image coordinates of the depth image from an image coordinate system to a world coordinate system. The 3D point cloud data may describe a 3D structural feature of an object, that is, a 3D geometric feature in a 3D space, and each 3D point converted from a depth image back projection into a 3D space may correspond to each pixel of an original depth image.)
Regarding claim 4: Kao discloses: The data processing apparatus according to claim 3,
Kao further discloses: wherein the segmentation unit makes a number of divisions of the plurality of first distance image segment data and a number of divisions of the plurality of second distance image segment data different from each other.
Regarding claim 5: Kao discloses: The data processing apparatus according to claim 1,
Kao further discloses: wherein the converter performs at least two conversion processings of a plurality of conversion processings of respectively converting the plurality of first distance image segment data to the plurality of first point cloud segment data in parallel. ([0188] when the 3D detection result includes a 3D segmentation result and when the target image includes an object of an incomplete shape, an extraction result of an object included in a scene may be acquired based on the 3D point cloud data. [0189] In other words, the image processing method according to an example may complete 3D point cloud data corresponding to the object of the incomplete shape based on the object of the incomplete shape, may acquire the completed 3D point cloud data, and may acquire the extraction result of the object included in the scene based on the completed 3D point cloud data.)
Regarding claim 6: Kao discloses: The data processing apparatus according to claim 1,
Kao further discloses: wherein the segmentation unit divides at least a part of color image data including color information of the measurement point in the measurement space into a plurality of color image segment data depending on the distance information corresponding to the color information,the plurality of first distance image segment data and the plurality of color image segment data constitute a plurality of segment data sets in which each set includes one first distance image segment data and one color image segment data of a same distance section, and the converter individually converts each of the plurality of segment data sets to the first point cloud segment data including the coordinate information and the color information of the measurement point in the measurement space. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm [0084] one method of determining the 3D point cloud data corresponding to the depth image based on the depth image is to convert depth information and 2D image coordinates of the depth image from an image coordinate system to a world coordinate system. The 3D point cloud data may describe a 3D structural feature of an object, that is, a 3D geometric feature in a 3D space, and each 3D point converted from a depth image back projection into a 3D space may correspond to each pixel of an original depth image.)
Regarding claim 7: Rejected using the same rationale as claim 1.
Regarding claim 8: Kao discloses: The data processing apparatus according to claim 7,
Kao further discloses: wherein the converter converts first partial distance image data including the distance information related to a first object in the measurement space in the distance image data to the first point cloud data, and the segmentation unit divides the first point cloud data into the plurality of first point cloud segment data. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm [0084] one method of determining the 3D point cloud data corresponding to the depth image based on the depth image is to convert depth information and 2D image coordinates of the depth image from an image coordinate system to a world coordinate system. The 3D point cloud data may describe a 3D structural feature of an object, that is, a 3D geometric feature in a 3D space, and each 3D point converted from a depth image back projection into a 3D space may correspond to each pixel of an original depth image.)
Regarding claim 9: Kao discloses: The data processing apparatus according to claim 8,
Kao further discloses: wherein the converter converts second partial distance image data including the distance information related to a second object in the measurement space in the distance image data to second point cloud data including the coordinate information of the measurement point in the measurement space, and the segmentation unit divides the second point cloud data into a plurality of second point cloud segment data depending on the coordinate value of the measurement space in the depth direction. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm [0084] one method of determining the 3D point cloud data corresponding to the depth image based on the depth image is to convert depth information and 2D image coordinates of the depth image from an image coordinate system to a world coordinate system. The 3D point cloud data may describe a 3D structural feature of an object, that is, a 3D geometric feature in a 3D space, and each 3D point converted from a depth image back projection into a 3D space may correspond to each pixel of an original depth image.)
Regarding claim 10: Kao discloses: The data processing apparatus according to claim 7,
Kao further discloses: wherein the segmentation unit divides first partial point cloud data including the coordinate information related to a first object in the measurement space in the first point cloud data into the plurality of first point cloud segment data. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm.)
Regarding claim 11: Kao discloses: The data processing apparatus according to claim 10,
Kao further discloses: wherein the segmentation unit divides second partial point cloud data including the coordinate information related to a second object in the measurement space in the first point cloud data into a plurality of second point cloud segment data depending on the coordinate value of the measurement space in the depth direction. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm [0084] one method of determining the 3D point cloud data corresponding to the depth image based on the depth image is to convert depth information and 2D image coordinates of the depth image from an image coordinate system to a world coordinate system. The 3D point cloud data may describe a 3D structural feature of an object, that is, a 3D geometric feature in a 3D space, and each 3D point converted from a depth image back projection into a 3D space may correspond to each pixel of an original depth image.)
Regarding claim 12: Rejected using the same rationale as claim 4.
Regarding claim 13: Kao discloses: The data processing apparatus according to claim 7,
Kao further discloses: wherein the converter converts at least a part of a data set including color image data including color information and the distance image data of the measurement point in the measurement space to the first point cloud data including the coordinate information and the color information of the measurement point in the measurement space.
Regarding claim 14: Kao discloses: The data processing apparatus according to claim 3,
Kao further discloses: wherein the point cloud processing unit individually performs processing on each of a plurality of second point cloud segment data obtained in the converter. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm)
Regarding claim 15: Kao discloses: The data processing apparatus according to claim 3,
Kao further discloses: further comprising a combining unit configured to combine the plurality of first point cloud segment data and the plurality of second point cloud segment data. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm [0144] In the method of FIG. 2, the final prediction result may be detected using multi-area prediction in the feature maps P3, P4, P5 and P6 221, 222, 223 and 224. For each updated anchor 225, a location and a label of a category may be represented by a vector. The location and the label of the category may be simultaneously predicted. To obtain prediction sensitive to a location, adjacent grids in addition to an intermediate grid may be used for each anchor. In an example, for convenience, both the intermediate grid and a surrounding grid may be called adjacent grids. For each anchor, when a combined feature map, that is, the feature map P3 221 is obtained, prediction thereof may be obtained by statistics through prediction of a plurality of grids.)
Regarding claim 16: Kao discloses: The data processing apparatus according to claim 15,
Kao further discloses: wherein the point cloud processing unit does not perform processing on at least one second point cloud segment data of the plurality of second point cloud segment data obtained in the converter, and the combining unit combines the plurality of first point cloud segment data processed in the point cloud processing unit and the plurality of second point cloud segment data including the at least one second point cloud segment data being unprocessed. ([0188] when the 3D detection result includes a 3D segmentation result and when the target image includes an object of an incomplete shape, an extraction result of an object included in a scene may be acquired based on the 3D point cloud data. [0189] In other words, the image processing method according to an example may complete 3D point cloud data corresponding to the object of the incomplete shape based on the object of the incomplete shape, may acquire the completed 3D point cloud data, and may acquire the extraction result of the object included in the scene based on the completed 3D point cloud data.)
Regarding claim 17: Rejected using the same rationale as claim 5.
Regarding claim 18: Kao discloses: The data processing apparatus according to claim 1,
Kao further discloses: wherein the point cloud processing unit makes processing parameters different between the processings for the at least two first point cloud segment data of the plurality of first point cloud segment data. ([0149] The predictors 231 and 233 may be defined for each of the “K” adjacent grids. Each of the predictors 231 and 233 may interpret information of a corresponding grid only. For example, a predictor of an upper grid may use only feature information around the upper grid. Similarly, a predictor of another grid may use only feature information around the other grid. Generally, all information may be inferred using area features. For example, when an area of a head is given, a location of the entire object may be inferred. Thus, a predictor of an adjacent grid may infer information of an object from a central grid. Even when a portion of an area is occluded, strong prediction may be obtained through prediction of other areas. The “K” adjacent grids may correspond to the same anchor. In other words, the “K” adjacent grids may have the same anchor parameters including a location (x,y), a width and a height. [0149]Anchors may be different in size. A relatively large anchor may tend to fall into an object area, and adjacent grids may tend to represent a portion of object information. In other words, the method disclosed herein may be similar to segmentation of an object. In this example, even when a portion of the object is occluded, the entire object may be detected by other portions. In the case of a relatively small anchor, adjacent grids may tend to include a portion of an appearance of an object and surrounding environment information. Since environment information is very useful for distinguishing relatively small objects, the above strategy is very effective in detecting relatively small objects.)
Regarding claim 19: Kao discloses: A robot control system comprising: the segmentation unit, the converter, and the point cloud processing unit included in the data processing apparatus according to claim 1;
Kao further discloses: and a robot controller configured to control a robot, based on the plurality of first point cloud segment data processed in the point cloud processing unit or combined point cloud data obtained by combining the plurality of first point cloud segment data. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm [0144] In the method of FIG. 2, the final prediction result may be detected using multi-area prediction in the feature maps P3, P4, P5 and P6 221, 222, 223 and 224. For each updated anchor 225, a location and a label of a category may be represented by a vector. The location and the label of the category may be simultaneously predicted. To obtain prediction sensitive to a location, adjacent grids in addition to an intermediate grid may be used for each anchor. In an example, for convenience, both the intermediate grid and a surrounding grid may be called adjacent grids. For each anchor, when a combined feature map, that is, the feature map P3 221 is obtained, prediction thereof may be obtained by statistics through prediction of a plurality of grids. [0279] A user may trigger a deformation request of the target object included in the target image using an AR controller 1730. For example, the user may trigger the deformation request based on the virtual object in a scene corresponding to the target image. The deformation request may include deformation information.)
Regarding claim 20: Kao discloses: The robot control system according to claim 19,
Kao further discloses: wherein the robot controller recognizes an object in the measurement space, based on the plurality of first point cloud segment data or the combined point cloud data. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm [0144] In the method of FIG. 2, the final prediction result may be detected using multi-area prediction in the feature maps P3, P4, P5 and P6 221, 222, 223 and 224. For each updated anchor 225, a location and a label of a category may be represented by a vector. The location and the label of the category may be simultaneously predicted. To obtain prediction sensitive to a location, adjacent grids in addition to an intermediate grid may be used for each anchor. In an example, for convenience, both the intermediate grid and a surrounding grid may be called adjacent grids. For each anchor, when a combined feature map, that is, the feature map P3 221 is obtained, prediction thereof may be obtained by statistics through prediction of a plurality of grids. [0279] A user may trigger a deformation request of the target object included in the target image using an AR controller 1730. For example, the user may trigger the deformation request based on the virtual object in a scene corresponding to the target image. The deformation request may include deformation information.)
Regarding claim 21: Rejected using the same rationale as claim 1, however additionally directed to “A non-transitory computer-readable recording medium storing a program configured to cause a computer apparatus to”, which is further disclosed by Kao: A non-transitory computer-readable recording medium storing a program configured to cause a computer apparatus to ([0349] The instructions or software to control a processor or computer to implement the hardware components and perform the methods as described above, and any associated data, data files, and data structures, are recorded, stored, or fixed in or on one or more non-transitory computer-readable storage media. Examples of a non-transitory computer-readable storage medium include read-only memory (ROM))
Regarding claim 22: Rejected using the same rationale as claim 1.
Regarding claim 23: Rejected using the same rationale as claims 4 and 12.
Regarding claim 24: Kao discloses: The data processing apparatus according to claim 9,
Kao further discloses: wherein the point cloud processing unit individually performs processing on each of a plurality of second point cloud segment data obtained in the converter. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm [0144] In the method of FIG. 2, the final prediction result may be detected using multi-area prediction in the feature maps P3, P4, P5 and P6 221, 222, 223 and 224. For each updated anchor 225, a location and a label of a category may be represented by a vector. The location and the label of the category may be simultaneously predicted. To obtain prediction sensitive to a location, adjacent grids in addition to an intermediate grid may be used for each anchor. In an example, for convenience, both the intermediate grid and a surrounding grid may be called adjacent grids. For each anchor, when a combined feature map, that is, the feature map P3 221 is obtained, prediction thereof may be obtained by statistics through prediction of a plurality of grids.)
Regarding claim 25: Rejected using the same rationale as claim 24.
Regarding claim 26: Kao discloses: The data processing apparatus according to claim 9,
Kao further discloses: further comprising a combining unit configured to combine the plurality of first point cloud segment data and the plurality of second point cloud segment data. ([0077] an image (hereinafter, referred to as a “target image”) to be processed may be acquired, 3D point cloud data corresponding to a depth image in the target image may be determined, and an object extraction result may be acquired based on the 3D point cloud data. The object extraction result may be a result obtained by extracting an object included in a scene. The 3D point cloud data may be a point set including a plurality of 3D discrete points, and a quantity of the 3D point cloud data may be less than a quantity of data corresponding to 3D voxels. Thus, by acquiring the object extraction result based on the 3D point cloud data, it is possible to save a storage space, to reduce a data workload, and to enhance a work efficiency of an algorithm. Also, the 3D point cloud data may describe a 3D structure feature of an object, and the object extraction result based on the 3D point cloud data may be more exact. An MLP encoder may be used to extract a feature of 3D point cloud data, and may convert the 3D point cloud data into a matrix, to further reduce a data throughput and enhance an efficiency of an algorithm [0144] In the method of FIG. 2, the final prediction result may be detected using multi-area prediction in the feature maps P3, P4, P5 and P6 221, 222, 223 and 224. For each updated anchor 225, a location and a label of a category may be represented by a vector. The location and the label of the category may be simultaneously predicted. To obtain prediction sensitive to a location, adjacent grids in addition to an intermediate grid may be used for each anchor. In an example, for convenience, both the intermediate grid and a surrounding grid may be called adjacent grids. For each anchor, when a combined feature map, that is, the feature map P3 221 is obtained, prediction thereof may be obtained by statistics through prediction of a plurality of grids. [0279] A user may trigger a deformation request of the target object included in the target image using an AR controller 1730. For example, the user may trigger the deformation request based on the virtual object in a scene corresponding to the target image. The deformation request may include deformation information.)
Regarding claim 27: Rejected using the same rationale as claim 26.
Regarding claim 28: Rejected using the same rationale as claim 5 and 17.
Regarding claim 29: Rejected using the same rationale as claim 18.
Regarding claim 30: Rejected using the same rationale as claim 19.
Regarding claim 31: Rejected using the same rationale as claim 20.
Conclusion
The prior art made of record, and not relied upon, considered pertinent to applicant' s disclosure or directed to the state of art is listed on the enclosed PTO-892.
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/ATTICUS A CAMERON/ /JASON HOLLOWAY/ Primary Examiner, Art Unit 3658 Examiner, Art Unit 3658A