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 .
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(s) 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, opinion, organizing human activity and mathematical concepts and calculations). The claim(s) recite(s) a method, a system, and a CRM for surface matching to identify objects. . This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved .The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such except for the generic computer elements at high level of generality (i.e., processor, memory).
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claims 1, 10, and 19 are directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories?
YES.
Claim(s) 1, 10, and 19 are directed to a method, i.e. process, an apparatus, i.e. a system, and a CRM, respectively.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?
YES.
The claims are directed toward a mental process (i.e. abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The claims comprise a mental process that can be practicably performed in the human mind (or generic computers or components configured to perform the method) and, therefore, an abstract idea.
Regarding Claim(s) 1, 10, and 19: the method recites the steps (functions) of:
ranking, (mental process including observation and evaluation, and can be done mentally in the human mind);
determining, (mental process including observation and evaluation, and can be done mentally in the human mind);
grouping, (mental process including observation and evaluation, and can be done mentally in the human mind);
determining, (mental process including observation and evaluation, and can be done mentally in the human mind);
selecting, (mental process including observation and evaluation, and can be done mentally in the human mind);
performing, (mental process including observation and evaluation, and can be done mentally in the human mind).
These limitations, as drafted, is a simple process that, under their broadest reasonable interpretation, covers performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
As such, a person could score and filters possible object occurrences, group similar ones, then pick the best in each group using a second score, and finally verifies each picked candidate by matching it to the surface of a stored 3D model either mentally or using a pen and paper. The mere nominal recitation that the various steps are being executed by a device/in a device (e.g. processing unit) does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO.
The claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claim(s) 1, 10, and 19 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. The claims recite(s) the further limitations of:
obtaining, by a three-dimensional (3D) camera, a 3D image of a field of view of the 3D camera, the 3D image including one or more occurrence candidates of one or more objects present in a scene (insignificant pre -solution extra activity of gathering data using generic computers or components);
processor (generic computers or components configured to perform the steps);
a three-dimensional (3D) imager, and a processor and computer-readable media storage (generic computers or components configured to perform the steps).
These limitations are recited at a high level of generality (i.e. as a general action or change being taken based on the results of the acquiring step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity. Further, the claims are claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
NO.
The claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claim(s) 1, 10, and 19 do not recite any additional elements that are not well-understood, routine or conventional. The use of a computer to rank, determine, group, select, and perform as claimed in Claim(s) 1, 10, and 19 are a routine, well-understood and conventional process that is performed by computers.
Thus, since Claim(s) 1, 10, and 19 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that Claim(s) 1, 10, and 19 are not eligible subject matter under 35 U.S.C 101.
Regarding claims 2-9, 11-18, and 20-22: the additional limitations do not integrate the mental process into practical application or add significantly more to the mental process. The limitation(s): fall within the categories of (mental process including observation and evaluation, and can be done mentally in the human mind) OR (mathematical concepts, mathematical relationships, mathematical formulas or equations, mathematical calculations) OR (insignificant pre/post-solution extra activity of gathering/generating data) OR (generic computers or components configured to perform the steps).
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1, 10, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Drost (US 20110273442).
Regarding claim 1:
Drost discloses: a method (FIGS. 4 and 5), comprising:
obtaining, by a three-dimensional (3D) camera, a 3D image of a field of view of the 3D camera (¶ [0010] “…In a typical application the 3D scene is acquired using, for example, stereo with two or more cameras, sheet-of-light, time-of-flight, depth from focus, or photometric stereo”);
the 3D image including one or more occurrence candidates of one or more objects present in a scene (¶ [0014] “According to a first aspect the invention provides a method for recognizing instances of a 3D object in 3D scene data and for determining the 3D poses of said instances comprising the following steps: (a) providing 3D scene data; (b) selecting at least one reference point from the 3D scene data; (c) computing, for each selected reference point, pose candidates for the 3D object under the assumption that said reference point is part of the 3D object; (d) computing a set of filtered poses from the pose candidates.”)
ranking, via a processor, the one or more occurrence candidates (¶ [0022] “…step (d) the computation comprises (d1) defining a neighbor relation between the pose candidates; (d2) computing the score of each pose as the weighted sum of the scores of the neighboring pose candidates; (d3) selecting the set of filtered poses by ranking the poses by the score computed in (d2).”);
determining, via the processor and based on a first metric, a set of occurrence candidates from the ranked one or more occurrence candidates (¶ [0019] “…(c2) creating a counter for each pose space sample of step (c1); … (c5) increasing, for each pose computed in step (c4), the counter for the corresponding pose space sample; and (c6) detecting peak counter values in the sampled pose space and selecting the corresponding pose space samples as pose candidates.”; ¶ [0060] “…each of which is returned with the counter value of the corresponding local coordinate sample. The counter value is the score of said 3D pose”; The first metric is the counter/score with a threshold; [peaks select the set. Because the counter is expressly the score, candidate at this stage are score-bearing (rankable); this the set is determined from ranked/score-bearing candidates under BRI.);
grouping, via the processor, occurrence candidates of the determined set of occurrence candidates based on at least one attribute of each occurrence candidate (¶ [0022] “…(d1) defining a neighbor relation between the pose candidates… Preferably, the neighborhood relation is defined by thresholding the difference in the translation of the poses and the rotation of the poses or by thresholding the maximum distance that a point on the 3D object can have under both poses;” )
determining, via the processor, a confidence level of each group of occurrence candidates based on a number of occurrence candidates in each group (¶ [0022] “…(d2) computing the score of each pose as the weighted sum of the scores of the neighboring pose candidates; (d3) selecting the set of filtered poses by ranking the poses by the score computed in (d2).”; ¶ [0064] “…(d2) assigning a new score to each pose that is the sum over all scores of neighboring (as defined in (d1)) poses; (d3) sorting the poses by the new score; (d4) selecting the poses with the best scores; (d5) optionally recomputing the selected poses by averaging over the neighboring poses” The neighbor-weighted sum is computed over the occurrence candidates in the group; as the number of neighboring candidates increases; the sum (confidence) increases);
selecting, via the processor and based on a second metric, an occurrence candidate having a highest second metric value from each group of occurrence candidates (¶ [0022] “…(d2) computing the score of each pose as the weighted sum of the scores of the neighboring pose candidates; (d3) selecting the set of filtered poses by ranking the poses by the score computed in (d2).”; ¶ [0064] “…(d2) assigning a new score to each pose that is the sum over all scores of neighboring (as defined in (d1)) poses; (d3) sorting the poses by the new score; (d4) selecting the poses with the best scores; (d5) optionally recomputing the selected poses by averaging over the neighboring poses” After grouping, Drost applied a s second scoring (neighbor-weighted) within the group, then select the highest (a second metric) per group); and
performing, via the processor, matching of the selected occurrence candidates with a surface of a 3D model (¶ [0066] “Scoring is a method that takes as input the final pose as calculated in the algorithm as well as the 3D scene data and the 3D object data, and that outputs one or more values that describe the quality of the calculated pose or the consistency between scene and object under said pose.”; ¶ [0067] “…(c) counting the number of scene points that lie on the model surface given the resulting pose”).
Regarding claims 10 and 19: the claims limitations are similar to those of claim 1; therefore, rejected in the same manner as applied above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-3, 6, 11-12, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Drost (US 20110273442) in view of Bergen (US 20210073571).
Regarding claim 2:
Drost discloses the limitations of claim 1 as applied above.
Drost does not specifically teach: wherein the occurrence candidates are 3D points indicative of a surface of the one or more objects present in the scene.
However, in a related field, Bergen teaches: wherein the occurrence candidates are 3D points indicative of a surface of the one or more objects present in the scene (abstract: “…The point clouds are converted into logical arrays for ease of processing. Then the logical arrays are compared (e.g. using the AND function and counting matches between the two logical arrays).”; ¶ [0011] “…The point clouds are converted from their analog form into regularly spaced logical arrays at multiple resolutions. The logical arrays are then compared at the various resolutions and a set of comparison response values is determined.” "logical arrays" are used to represent the spatial presence of points within a structured grid, effectively creating voxel bins).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Drost to incorporate the teachings of Bergen by including: wherein the occurrence candidates are 3D points indicative of a surface of the one or more objects present in the scene in order to represent candidates in the same point/voxel form to enable logical AND plus count overlap tests on voxel/bit arrays. This is a well-known and faster implementation that yields the same type of point-count evidence Drost relies on, while improving memory locality and robustness to sensor noise.
Regarding claim 3:
Drost discloses the limitations of claim 1 as applied above.
Drost further discloses: applying, via the processor, a first transformation to the ranked one or more occurrence candidates (¶ [0060] “One local coordinate is taken from each selected sample, and the local coordinates are transformed into full 3D poses, each of which is returned with the counter value of the corresponding local coordinate sample. The counter value is the score of said 3D pose.”);
Drost does not specifically teach: wherein determining, via the processor and based on the first metric, the set of occurrence candidates from the ranked one or more occurrence candidates comprises: determining, via the processor, one or more 3D model voxel bins; applying, via the processor, a first transformation to the ranked one or more occurrence candidates; generating, via the processor, one or more first scene voxel bins based on the applied first transformation; determining, via the processor, one or more filled first scene voxel bins; and determining, via the processor, a value of the first metric for each ranked occurrence candidate, the value of the first metric being indicative of a confidence level associated with a correspondence between respective filled first scene voxel bins and respective 3D model voxel bins
However, Bergen teaches: wherein determining, via the processor and based on the first metric, the set of occurrence candidates from the ranked one or more occurrence candidates comprises:
determining, via the processor, one or more 3D model voxel bins (abstract “..The point clouds are converted into logical arrays for ease of processing.”; ¶ [0048] “ FIG. 3 is a diagram showing the step of voxelizing the point cloud of FIG. 2 “; "logical arrays" are used to represent the spatial presence of points within a structured grid, effectively creating voxel bins);
generating, via the processor, one or more first scene voxel bins based on the applied first transformation (abstract “..The point clouds are converted into logical arrays for ease of processing.”; ¶ [0048] “ FIG. 3 is a diagram showing the step of voxelizing the point cloud of FIG. 2 “; ¶ [0050] “…a logical array is built based upon the voxelized point clouds. We create a logical array the same dimension as the voxel grid. Then we assign a value of true for each logical array element where the analogous voxel grid has a point… All empty voxels result in a value of false in the logical array “); "logical arrays" are used to represent the spatial presence of points within a structured grid, effectively creating voxel bins);
determining, via the processor, one or more filled first scene voxel bins (abstract: “…The point clouds are converted into logical arrays for ease of processing. Then the logical arrays are compared (e.g. using the AND function and counting matches between the two logical arrays).”; it is implied from the AND function that 1 is filled and 0 is empty)and
determining, via the processor, a value of the first metric for each ranked occurrence candidate, the value of the first metric being indicative of a confidence level associated with a correspondence between respective filled first scene voxel bins and respective 3D model voxel bins (abstract: “…The point clouds are converted into logical arrays for ease of processing. Then the logical arrays are compared (e.g. using the AND function and counting matches between the two logical arrays).”; ¶ [0011] “…The point clouds are converted from their analog form into regularly spaced logical arrays at multiple resolutions. The logical arrays are then compared at the various resolutions and a set of comparison response values is determined.” Bergen provides a first metric that measures the correspondence between respective filled first scene voxel bins and respective 3D model voxel bins).
Regarding claim 6:
Drost in view of Bergen teaches the limitations of claim 3 as applied above.
Bergen further teaches: wherein determining, via the processor, the value of the first metric indicative of the confidence level associated with the correspondence between the respective first scene voxel bins and the respective 3D model voxel bins comprises: determining, via the processor, a binary intersection between respective filled first scene voxel bins and respective 3D model voxel bins (¶ [0014] “The logical arrays are compared using logical operations. For example, comparison response values may be obtained by performing logical AND functions between arrays and counting true results against the total of combined points”; ¶ [0016] “Translation aligning may be performed by offsetting the logical arrays…performing logical AND functions between arrays and counting true results after each offset, and determining the offset that maximizes the results”; ¶ [0067] “…A typical flow for object comparison is to choose a quantization level and compare the point clouds of two objects' data arrays (using the AND function and summing). Then the quantization level is changed and the comparison is repeated several times”; ¶ [0069] “…perform the logical AND operation between them. The resulting bits that are on (true) represent the points that the two point clouds have in common at the quantization level used. The percent match is found simply by dividing the number of matching points by the number of unique total points including both point clouds”).
Regarding claims 11-12, 15, and 20: the claims limitations are similar to those of claims 2, 3, 6, and 3, respectively; therefore, rejected in the same manner as applied above.
Claim(s) 4, 13, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Drost (US 20110273442) in view of Bergen (US 20210073571) and Finley (US 20220343518).
Regarding claim 4:
Drost in view of Bergen teaches the limitations of claim 3 as applied above.
Dorst further discloses: wherein applying, via the processor, the first transformation to the ranked one or more occurrence candidates comprises:
determining, via the processor, one or more ranked occurrence candidates matching with the surface of the 3D model (¶ [0060] “One local coordinate is taken from each selected sample, and the local coordinates are transformed into full 3D poses, each of which is returned with the counter value of the corresponding local coordinate sample. The counter value is the score of said 3D pose.”);; ¶ [0066] “Scoring is a method that takes as input the final pose as calculated in the algorithm as well as the 3D scene data and the 3D object data, and that outputs one or more values that describe the quality of the calculated pose or the consistency between scene and object under said pose.”; ¶ [0067] “…(c) counting the number of scene points that lie on the model surface given the resulting pose”);
Drost in view of Bergen does not specifically teach: determining, via the processor, whether a number of the ranked occurrence candidates matching with the surface of the 3D model is greater than a threshold; and determining, via the processor, a second transformation when the number of ranked occurrence candidates matching with the surface of the 3D model is greater than the threshold.
However, in a related fields Finley teaches: determining, via the processor, whether a number of the ranked occurrence candidates matching with the surface of the 3D model is greater than a threshold (¶ [0018] “…and finding an optimal registration transformation among a plurality of combinations, wherein each combination includes at least three points in the first coordinate system and corresponding points in the second coordinate system determined by the second image capturing device”; ¶ [0035] “…the systems and methods may create a connected components list of candidate voxels… For example, an intensity threshold to the scan with a predetermined value can be applied to create a binary mask. A connected components algorithm can be applied afterwards to have a list of binary voxels with a common connectivity”; ¶ [0036] “Optionally, a filter based on the size (e.g., number of voxels) of each connected components can be used to filter noise. The output at this step can be a list of candidate voxels blobs 202, as shown in FIGS. 2A-2B.”); and
determining, via the processor, a second transformation when the number of ranked occurrence candidates matching with the surface of the 3D model is greater than the threshold (¶ [0018] “…and finding an optimal registration transformation among a plurality of combinations, wherein each combination includes at least three points in the first coordinate system and corresponding points in the second coordinate system determined by the second image capturing device” and FIGS. 6-7).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Drost and Bergen to incorporate the teachings of by including: determining, via the processor, whether a number of the ranked occurrence candidates matching with the surface of the 3D model is greater than a threshold; and determining, via the processor, a second transformation when the number of ranked occurrence candidates matching with the surface of the 3D model is greater than the threshold in order to compute a second registration after threshold gating of candidate to avoid refining on insufficient evidence. Applying this technique is a well-known technique in 3D alignment/registration that yields to a predictable results.
Regarding claims 13 and 21: the claims limitations are similar to those of claim 4; therefore, rejected in the same manner as applied above.
Claim(s) 7-9, 16-18, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Drost (US 20110273442) in view of Zhu (US 20210350165).
Regarding claim 7:
Drost discloses the limitations of claim 1 as applied above.
Drost does not specifically teach: wherein the at least one attribute is a centroid location of each occurrence candidate.
However, in a related field, Zhu teaches: wherein the at least one attribute is a centroid location of each occurrence candidate (¶ [0046] “…the machine vision system determines a reference (e.g., a reference plane, a line, a centroid, and/or the like) that is disposed in some spatial relation to the 3D point cloud (e.g., selected based on the point cloud, computed based on the point cloud, and/or the like)”; ¶ [0048] “…for each voxel of the 3D voxel grid, a single 3D data point for the voxel based on the associated set of 3D data points, and store the single 3D data point in the voxel (e.g., by determining a centroid of the points, averaging point values, etc.).”; ¶ [0052] “…the reference can be a point estimated based on the 3D point cloud, such as an estimated center of mass or centroid of the 3D point cloud. The machine vision system can process the 3D point cloud (e.g., the 3D points and/or voxels) to determine the estimated center of mass, and use the estimated center of mass as the reference point”).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Drost to incorporate the teachings of Zhu by including: wherein the at least one attribute is a centroid location of each occurrence candidate in order to improve spatial stability.
Regarding claim 8:
Drost discloses the limitations of claim 1 as applied above.
Zhu further teaches: wherein selecting, via the processor and based on the second metric, the occurrence candidate having a highest second metric value from each group of occurrence candidates comprises:
determining, via the processor, one or more 3D model voxel bins (¶ [0014] “…generating a 3D voxel grid for at least a portion of the 3D point cloud, wherein each voxel of the 3D voxel grid comprises a same set of dimensions, determining, for each voxel of the 3D voxel grid, whether one or more of the plurality of 3D data points is within the voxel… and storing the single 3D data point in the voxel”);
applying, via the processor, a second transformation to one or more point candidates present within a bounding box including the one or more objects present in the scene (¶ [0056] “…FIG. 3A also shows an exemplary histogram 350 of point to plane distances for the point cloud 352 of the three planar surfaces within the box 354 to the plane 356 (which is, again, disposed at the base of the box 354).”; ¶ [0089] “…the machine vision system can seek a rotation that best aligns the dominant directions, in order to minimize the difference in the 3D directions conveyed by the two sets of peak locations. The machine visions system can obtain a rigid transform corresponding to this best rotation.”)
generating, via the processor, one or more second scene voxel bins based on the applied second transformation (¶ [0014] “…generating a 3D voxel grid for at least a portion of the 3D point cloud, wherein each voxel of the 3D voxel grid comprises a same set of dimensions, determining, for each voxel of the 3D voxel grid, whether one or more of the plurality of 3D data points is within the voxel… and storing the single 3D data point in the voxel”);
determining, via the processor, one or more filled second scene voxel bins (¶ [0014] “…determining, for each voxel of the 3D voxel grid, whether one or more of the plurality of 3D data points is within the voxel… and storing the single 3D data point in the voxel”); and
determining, via the processor, a value of the second metric for each point candidate, the value of the second metric being indicative of a confidence level associated with a correspondence between respective filled second scene voxel bins and respective 3D model voxel bins (¶ [0057] “…the histograms 300 and 350 can be compared (e.g., to determine whether the objects captured by the 3D images are similar (or not))… The similarity score for this example was computed using a histogram intersection measure by comparing the sum of the minimums of the normalized frequencies over all of the bins between the two histograms”; ¶ [0058] “…The similarity score between the two histograms 300 and 380 is 0.191645, where the computed score value can range from 0 to 1.0, with 1.0 indicating the two compared histograms are identical, while a value of 0 indicates the least similarity.”).
Regarding claim 9:
Drost discloses the limitations of claim 1 as applied above.
Zhu further teaches: wherein determining, via the processor, the value of the second metric indicative of the confidence level associated with the correspondence between the respective second scene voxel bins and the respective 3D model voxel bins comprises: determining a histogram intersection between respective filled second scene voxel bins and respective 3D model voxel bins (¶ [0057] “…the histograms 300 and 350 can be compared (e.g., to determine whether the objects captured by the 3D images are similar (or not))… The similarity score for this example was computed using a histogram intersection measure by comparing the sum of the minimums of the normalized frequencies over all of the bins between the two histograms”; ¶ [0058] “…The similarity score between the two histograms 300 and 380 is 0.191645, where the computed score value can range from 0 to 1.0, with 1.0 indicating the two compared histograms are identical, while a value of 0 indicates the least similarity.”).
Regarding claims 16-18, and 22: the claims limitations are similar to those of claims 7-9, and 8, respectively, therefore, rejected in the same manner as applied above.
Notes
No prior art has been applied to claims 5 and 14.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WASSIM MAHROUKA whose telephone number is (571)272-2945. The examiner can normally be reached Monday-Thursday 8:00-5:00 EST.
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/WASSIM MAHROUKA/Primary Examiner, Art Unit 2665