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.
35 U.S.C. 101 requires that a claimed invention must fall within one of the four eligible categories of invention (i.e. process, machine, manufacture, or composition of matter) and must not be directed to subject matter encompassing a judicially recognized exception as interpreted by the courts. MPEP 2106. Three categories of subject matter are found to be judicially recognized exceptions to 35 U.S.C. § 101 (i.e. patent ineligible) (1) laws of nature, (2) physical phenomena, and (3) abstract ideas. MPEP 2106(II). To be patent-eligible, a claim directed to a judicial exception must as whole be directed to significantly more than the exception itself. See 2014 Interim Guidance on Patent Subject Matter Eligibility, 79 Fed. Reg. 74618, 74624 (Dec. 16, 2014). Hence, the claim must describe a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception. Id
Claims 1-4, 6-7, 9-12, 14-15 and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more. Claim 1 is directed to obtain a plurality of confidence maps associated with different classes of objects, . . .; determine maximum values from first position of a first and a second map; and generate a combined confidence map for the plurality of confidence maps, without additional elements that are sufficient to amount to significantly more than the judicial exception. Specifically, obtain a plurality of confidence maps associated with different classes of objects, . . ., is referring to gathering data under insignificant extra-solution activity specifically, pre-solution activity (MPEP 2106.05(g)), determine a maximum values from first position of a first and a second map, referring to metal process, concept that is performed in human mind (including an observation, evaluation, judgment, opinion) (MPEP § 2106.04(a)(2); and generate a combined confidence map for the plurality maps, referring to additional mental process that can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(III). Therefore, claim 1 meets the requirement of step 2A prong 1 for reciting abstract idea. Claim 1 further fails the requirement of step 2A prong 2, for lacking additional elements that integrate the judicial exception into practical Application. While the claim includes elements i.e. a memory and processor to assist in performing the functions of the claimed invention, the elements are treated as using a computer as a tool to perform a mental process (MPEP 2106.04(a)(2)(III)(C)(3)). An example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-5.
Claim 1 is further considered under step 2b of the guidelines, for whether the claim as a whole include additional elements that amount to significantly more than the judicial exception. Limitations that the courts have found to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Improvements to the functioning of a computer, e.g., a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, as discussed in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258-59, 113 USPQ2d 1097, 1106-07 (Fed. Cir. 2014) (see MPEP § 2106.05(a));
ii. Improvements to any other technology or technical field, e.g., a modification of conventional rubber-molding processes to utilize a thermocouple inside the mold to constantly monitor the temperature and thus reduce under- and over-curing problems common in the art, as discussed in Diamond v. Diehr, 450 U.S. 175, 191-92, 209 USPQ 1, 10 (1981) (see MPEP § 2106.05(a));
iii. Applying the judicial exception with, or by use of, a particular machine, e.g., a Fourdrinier machine (which is understood in the art to have a specific structure comprising a headbox, a paper-making wire, and a series of rolls) that is arranged in a particular way to optimize the speed of the machine while maintaining quality of the formed paper web, as discussed in Eibel Process Co. v. Minn. & Ont. Paper Co., 261 U.S. 45, 64-65 (1923) (see MPEP § 2106.05(b));
iv. Effecting a transformation or reduction of a particular article to a different state or thing, e.g., a process that transforms raw, uncured synthetic rubber into precision-molded synthetic rubber products, as discussed in Diehr, 450 U.S. at 184, 209 USPQ at 21 (see MPEP § 2106.05(c));
v. Adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (see MPEP § 2106.05(d)); or
vi. Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, e.g., an immunization step that integrates an abstract idea of data comparison into a specific process of immunizing that lowers the risk that immunized patients will later develop chronic immune-mediated diseases, as discussed in Classen Immunotherapies Inc. v. Biogen IDEC, 659 F.3d 1057, 1066-68, 100 USPQ2d 1492, 1499-1502 (Fed. Cir. 2011) (see MPEP § 2106.05(e)).
In the instant case claim 1 fails to meet any of the above conditions to further qualify under step 2B. Therefore, claim 1 is not eligible under 101.
Claim 2 reciting determine a respective maximum value from each position in the plurality of confidence maps, referring to mental process through observation of data and judgment; and include each respective maximum value in each position of the combined confidence map, as referring to further mental process and pen and paper. Therefore, claim 2 is not eligible under 101.
Claim 3 reciting the plurality of confidence maps is output by a machine learning model, referring to gathering data through mathematical process under Extra-solution activity. Therefore, claim 2 is not eligible under 101.
Claim 4 reciting wherein the at least one processor is configured to: process the combined confidence map to determine a plurality of bounding boxes for the different classes of objects, as referring to mental process drawing boxes. Therefore, the claim is not eligible under 101.
Claim 6 reciting determine a number of rows of confidence values and a number of columns of confidence values in the combined confidence map including maximum confidence values, as referring to mental process through observation and evaluation and; and reduce, based on the number of rows and the number of columns in the combined confidence map including maximum confidence values, a size of the combined confidence map to generate a reduced-size confidence map, referring to mental process of evaluation and judgment. Therefore, the claim is not eligible under 101.
Claim 7 reciting wherein the at least one processor is configured to: process the reduced-size confidence map to determine a plurality of bounding boxes for the different classes of objects, referring to mental process of observation, evaluation and opinion. Therefore, the claim is not eligible under 101.
Regarding method claims 9-12 and 14-15, similar observations as the corresponding apparatus claims 1-4 and 6-7, respectively, are applied to these claims as well. Therefore, these claims are not eligible under 101.
Claim 17 reciting obtain a plurality of confidence maps associated with different classes of objects, . . ., is referring to gathering data under insignificant extra-solution activity specifically, pre-solution activity (MPEP 2106.05(g)); determine a number of rows of confidence values and a number of columns of confidence values in the confidence map including maximum confidence values, as referring to mental process through observation and evaluation; and reduce, based on the number of rows and the number of columns in the confidence map including maximum confidence values, a size of the confidence map to generate a reduced-size confidence map, referring to mental process of evaluation and judgment. Therefore, the claim is not eligible under 101.
Claim 18 reciting remove at least one row of confidence values and at least one column of confidence values from the confidence map that are not included in the number of rows of confidence values and the number of columns of confidence values in the confidence map including maximum confidence values, as referring to mental process of observation and judgment; and maintain, in the confidence map, the number of rows of confidence values and the number of columns of confidence values in the confidence map including maximum confidence values, as post-solution activity of outputting data under Extra solution activity. Therefore, the claim is not eligible under 101.
Claim 19 reciting wherein the confidence map is a combined confidence map generated based on a plurality of confidence maps associated with different classes of objects, as referring to mental process of evaluation of data and judgment. Therefore, the claim is not eligible under 101.
Claim 20 reciting obtain the plurality of confidence maps, referring to gathering data under extra-solution activity; determine a maximum value from at least a first value in a first position of a first confidence map and a second value in a first position of a second confidence map, referring to mental process of observation and evaluation; and generate the combined confidence map for the plurality of confidence maps at least in part by including the maximum value in a first position of the combined confidence map, referring to mental process of evaluation, opinion, and judgment using pen and paper. Therefore, the claim is not eligible under 101.
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.
Claims 1-4 and 9-12 are rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over US 2023/0162472 A1 to Gor et al (hereinafter ‘Gor’).
Regarding claim 1, Gor discloses an apparatus for processing one or more confidence maps (Para [0034], wherein a system is configured to execute the steps of: [0035] providing confidence heatmaps by the neural network), the apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor (Para [0033], wherein the computer program product includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor) configured to: obtain a plurality of confidence maps associated with different classes of objects, each different confidence map of the plurality of confidence maps being associated with a different class of object (Para [0025], wherein the neural network provides confidence heatmaps, refinement offset vectors, centroid offset vectors and/or centroid confidence heatmaps for different types of objects, as different class of objects. For example, a first type may be “human being” and a second type may be “vehicle”.), wherein each confidence map includes a plurality of confidence values in a plurality of positions, each confidence value in a respective position indicating a likelihood that an object of a particular class is located in the respective position (Para [0026] and [0071], wherein confidence heatmaps are grouped and/or labelled according to given types of keypoints. So, in other words, the keypoints may include an indicator as to which kind of keypoint is present at a certain position of the image (e.g. left elbow, right shoulder, etc.), and wherein as shown in FIG. 1, the neural network 2 provides different output information. First, the neural network 2 provides multiple confidence heatmaps CH. Each confidence heatmap CH defines a certain area on the image and is associated with a certain keypoint of an object (e.g. left elbow of a person). The confidence heatmap CH indicates that associated keypoint is located in the confidence heatmap CH.); determine a maximum value from at least a first value in a first position of a first confidence map and a second value in a first position of a second confidence map (Para [0040], wherein determining the location of maximum value in the respective confidence heatmaps, using the location of maximum value in the respective confidence heatmaps as rough keypoint locations); and generate a combined confidence map for the plurality of confidence maps at least in part by including the maximum value in a first position of the combined confidence map (Para [0095], wherein in FIG. 4, confidence heatmaps of the objects shown in FIG. 3, inherently as the combined confidence map, are highlighted by white ovals and centroids of the objects are highlighted by white squares.). In the event that Applicant disagrees that Gor inherently discloses generating combined confidence map for the plurality of the maps, Gor specifically discloses that the process of generating the maps could even be in parallel for each object type generating and hence combined (Para [0025], wherein the detection of different objects may be performed in parallel, i.e., the method may determine keypoints for different objects in a single detection procedure of the neural network.). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the data from each heatmap in order to generate a final map image as depicted in Fig. 4.
Regarding claim 2, Gor discloses wherein the at least one processor is configured to: determine a respective maximum value from each position in the plurality of confidence maps (Para [0026], wherein confidence heatmaps are grouped and/or labelled according to given types of keypoints. So, in other words, the keypoints may include an indicator as to which kind of keypoint is present at a certain position of the image (e.g. left elbow, right shoulder, etc.).); and include each respective maximum value in each position of the combined confidence map (Para [0091], wherein output information of the neural network 2 may be provided to a post-processing system 3 which is configured to process received information in order to determine keypoints, determine associations of keypoints to certain objects and determine existing connections between keypoints in order to build a skeleton of the object, inherently by combining data from the confidence heatmaps).
Regarding claim 3, Gor discloses wherein the plurality of confidence maps is output by a machine learning model (Para [0071], wherein as shown in FIG. 1, the neural network 2 provides different output information. First, the neural network 2 provides multiple confidence heatmaps CH. Each confidence heatmap CH defines a certain area on the image and is associated with a certain keypoint of an object (e.g. left elbow of a person)).
Regarding claim 4, Gor discloses wherein the at least one processor is configured to: process the combined confidence map to determine a plurality of bounding boxes for the different classes of objects (Para [0117], wherein ground truth data may provide a bounding box to each object indicating the extent of the object. Based on the bounding box, the centroid of an object can be determined, for example by calculating the center coordinate of the bounding box and using that center coordinate as the centroid location of the object.).
Regarding method claims 9-12, please refer to the rejections of corresponding apparatus claims 1-4 above, respectively, for further teachings.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Gor in view of US 2025/0209657 A1 to Ju.
Regarding claims 5 and 13, Gor discloses determine a particular number of positions in the combined confidence map having highest confidence scores (Para [0084], wherein each centroid refinement offset vector is associated with a certain centroid confidence heatmap. The centroid refinement offset vector provides correction information for correcting the position of maximum value of the associated centroid confidence heatmap in order to precisely define the position on the image at which the centroid is located); determine an initial plurality of bounding boxes for the particular number of positions (Para [0117], wherein based on the bounding box, the centroid of an object can be determined, for example by calculating the center coordinate of the bounding box and using that center coordinate as the centroid location of the object). Gor does not specifically disclose filter the initial plurality of bounding boxes to determine the plurality of bounding boxes. Ju discloses filter the initial plurality of bounding boxes to determine the plurality of bounding boxes (Para [0099], wherein the electronic device 2000 may remove a bounding box corresponding to a pixel having a heat map score less than or equal to a preset reference heat map score (e.g., 0.3), as the initial bounding boxes, among the top k pixels extracted based on the heat map score). Gor and Ju are combinable because they both disclose detecting objects in an image. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine the filter the initial plurality of bounding boxes to determine the plurality of bounding boxes of Ju’s apparatus with Gor’s in order to only detect pixels having the peak heat map score that correspond to the respective classes (Para [0060]).
Claims 6, 14 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gor in view of US 12,518,403 B2 to Nguyen.
Regarding claims 6 and 14, Gor does not specifically disclose wherein the at least one processor is configured to: determine a number of rows of confidence values and a number of columns of confidence values in the combined confidence map including maximum confidence values; and reduce, based on the number of rows and the number of columns in the combined confidence map including maximum confidence values, a size of the combined confidence map to generate a reduced-size confidence map. Nguyen discloses determine a number of rows of confidence values and a number of columns of confidence values in the combined confidence map including maximum confidence values (column 7, line 65 through column 8, line 11, wherein The target output is a soft assignment matrix . . . , where each row represents a track query, and each column represents an object query. We propose two choices for this layer: (1) dual-softmax . . .. For dual-softmax, as max confidence value, the algorithm performs a softmax operation on the input matrix along the rows (then sum of each row equal to 1), and along the columns (sum of each column equal to 1), yielding two matrices . . . and . . .. These two matrices are then element-wise multiplied with each other, yielding the output matrix as the predicted soft assignment matrix, . . . as combined map.); and reduce, based on the number of rows and the number of columns in the combined confidence map including maximum confidence values, a size of the combined confidence map to generate a reduced-size confidence map (column 8, lines 25-35, wherein the ground-truth assignment matrix Gass is a soft assignment matrix that is one-to-one mapped based on the multi-object tracking labels, satisfying: (i) ith track is linked to j th object if and . . .(each non-dustbin row or column will have a single element equal to 1 and all other elements equal to 0); (ii) tracks that do not correspond to any objects (i.e., disappeared or temporarily occluded objects) are matched with a dustbin object; (iii) objects that do not correspond to any tracks (i.e., the object appears for the first time) are matched with the dustbin track.), inherently as reducing size of the combined map based on the zeroed pixels. Gor and Nguyen are combinable because they both disclose detecting objects in an image. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine reduce a size of the combined confidence map to generate a reduced-size confidence map of Nguyen’s apparatus with Gor’s in order to remove background (column 8, lines 56-58).
Regarding claim 17, Gor discloses an apparatus for processing one or more confidence maps, the apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory (Para [0033], wherein the computer program product includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor), the at least one processor configured to: obtain a confidence map associated with a class of object (Para [0025], wherein the neural network provides confidence heatmaps, refinement offset vectors, centroid offset vectors and/or centroid confidence heatmaps for different types of objects, as different class of objects. For example, a first type may be “human being” and a second type may be “vehicle”.), the confidence map including a plurality of confidence values in a plurality of positions (Para [0026], wherein confidence heatmaps are grouped and/or labelled according to given types of keypoints. So, in other words, the keypoints may include an indicator as to which kind of keypoint is present at a certain position of the image (e.g. left elbow, right shoulder, etc.), each confidence value in a respective position of the confidence map indicating a likelihood that an object of the class is located in the respective position (Para [0071], wherein as shown in FIG. 1, the neural network 2 provides different output information. First, the neural network 2 provides multiple confidence heatmaps CH. Each confidence heatmap CH defines a certain area on the image and is associated with a certain keypoint of an object (e.g. left elbow of a person). The confidence heatmap CH indicates that associated keypoint is located in the confidence heatmap CH.)). Gor does not specifically disclose determine a number of rows of confidence values and a number of columns of confidence values in the combined confidence map including maximum confidence values; and reduce, based on the number of rows and the number of columns in the combined confidence map including maximum confidence values, a size of the combined confidence map to generate a reduced-size confidence map. Nguyen discloses determine a number of rows of confidence values and a number of columns of confidence values in the combined confidence map including maximum confidence values (column 7, line 65 through column 8, line 11, wherein The target output is a soft assignment matrix . . . , where each row represents a track query, and each column represents an object query. We propose two choices for this layers: (1) dual-softmax . . .. For dual-softmax, as max confidence value, the algorithm performs a softmax operation on the input matrix along the rows (then sum of each row equal to 1), and along the columns (sum of each column equal to 1), yielding two matrices . . . and . . .. These two matrices are then element-wise multiplied with each other, yielding the output matrix as the predicted soft assignment matrix, . . . as combined map.); and reduce, based on the number of rows and the number of columns in the combined confidence map including maximum confidence values, a size of the combined confidence map to generate a reduced-size confidence map (column 8, lines 25-35, wherein the ground-truth assignment matrix Gass is a soft assignment matrix that is one-to-one mapped based on the multi-object tracking labels, satisfying: (i) ith track is linked to j th object if and . . .(each non-dustbin row or column will have a single element equal to 1 and all other elements equal to 0); (ii) tracks that do not correspond to any objects (i.e., disappeared or temporarily occluded objects) are matched with a dustbin object; (iii) objects that do not correspond to any tracks (i.e., the object appears for the first time) are matched with the dustbin track.), inherently as reducing size of the combined map based on the zeroed pixels. Gor and Nguyen are combinable because they both disclose detecting objects in an image. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine reduce a size of the combined confidence map to generate a reduced-size confidence map of Nguyen’s apparatus with Gor’s in order to remove background (column 8, lines 56-58).
Regarding claim 18, in the combination of Gor and Nguyen, Nguyen further discloses wherein, to reduce the size of the confidence map based on the number of rows and the number of columns in the confidence map including maximum confidence values, the at least one processor is configured to: remove at least one row of confidence values and at least one column of confidence values from the confidence map that are not included in the number of rows of confidence values and the number of columns of confidence values in the confidence map including maximum confidence values (Fig. 5, the one last row of objects and last column of tracks in the soft assignment matrix); and maintain, in the confidence map, the number of rows of confidence values and the number of columns of confidence values in the confidence map including maximum confidence values (Fig. 5, the 3X4 post pruning matrix including all the available confidence values).
Regarding claim 19, in the combination of Gor and Nguyen, Gor further discloses wherein the confidence map is a combined confidence map generated based on a plurality of confidence maps associated with different classes of objects (Para [0095], wherein in FIG. 4, confidence heatmaps of the objects shown in FIG. 3, inherently as the combined confidence map, are highlighted by white ovals and centroids of the objects are highlighted by white squares.).
Regarding claim 20, in the combination of Gor and Nguyen, Gor further discloses wherein, to generate the combined confidence map, the at least one processor is configured to: obtain the plurality of confidence maps (Para [0025], wherein the neural network provides confidence heatmaps, refinement offset vectors, centroid offset vectors and/or centroid confidence heatmaps for different types of objects, as different class of objects. For example, a first type may be “human being” and a second type may be “vehicle”.); determine a maximum value from at least a first value in a first position of a first confidence map and a second value in a first position of a second confidence map (Para [0040], wherein determining the location of maximum value in the respective confidence heatmaps, using the location of maximum value in the respective confidence heatmaps as rough keypoint locations); and generate the combined confidence map for the plurality of confidence maps at least in part by including the maximum value in a first position of the combined confidence map (Para [0095], wherein in FIG. 4, confidence heatmaps of the objects shown in FIG. 3, inherently as the combined confidence map, are highlighted by white ovals and centroids of the objects are highlighted by white squares.).
Allowable Subject Matter
Claims 7 and 15 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include 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 or the prior art of record specifically, Gor and Nguyen, does not disclose:
. . . . wherein the at least one processor is configured to: process the reduced-size confidence map to determine a plurality of bounding boxes for the different classes of objects, of claims 7 and 15 combined with other features and elements of the claims;
Claims 8 and 16 are 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 or the prior art of record specifically, Gor and Nguyen, does not disclose:
. . . . wherein, to process the reduced-size confidence map to determine the plurality of bounding boxes for the different classes of objects, the at least one processor is configured to: determine a particular number of positions in the reduced-size confidence map having highest confidence scores; determine an initial plurality of bounding boxes for the particular number of positions; and filter the initial plurality of bounding boxes to determine the plurality of bounding boxes, of the claims 8 and 16 combined with other features and elements of the claims.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHERVIN K NAKHJAVAN whose telephone number is (571)272-5731. The examiner can normally be reached Monday-Friday 9:00-12:00 PST.
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/SHERVIN K NAKHJAVAN/Primary Examiner, Art Unit 2672