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 .
Drawings
The drawings are objected to because Figures 2, 4 and 15 are missing descriptors and/or labels. The Figures currently only include blocks with reference numerals. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 1-12 and 14-21 are objected to because of the following informalities:
Claims 1 (and corresponding dependent claims 2-10, 16-21), 11, 12 and 14 recite acronyms in parentheses but then these are repeated in further limitations. For instance convolutional neural network (CNN) is referred to either as convolutional neural network (CNN) or convolutional neural network CNN instead of simply CNN. Also, prediction map (PM) is repeated as prediction map PM. The first instance of these limitations should be listed with the acronym in claim 1, 11, 12 and 14.
Claim 3 recites “wherein, in step (a), the heads of the individuals of the images of the training set TDS are annotated using boxes centered on said heads, sizes of the boxes in superposed condition are reduced such that a distance, d, separating them is greater than a quarter of a smallest dimension, (L-a, L-b, H-a, H-b).” The dimensions listed are reference labels from Figure 7 and should be stricken similar to all the other reference labels and numerals.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: acquisition module in claim 14-15 ( paragraph 0077 – camera or digital photographic appliance (CCD or CMOS)).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12, 14-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 (and corresponding dependent claims 2-10 and 16-21) and claim 12 recite “…individuals in a crowd…one or more images of a crowd of individuals…provides, as output data, one or more binary images of connected components …corresponding to heads of the individuals of the crowd…composed of images of crowds of individuals whose heads are annotated…each image of a crowd…said image ….a binary image of connected components corresponding to the heads of the individuals.
The claim language includes multiple instances of a crowd, individuals, image, images, binary images. It is unclear if these instances are different or the same element.
Claim 11 recites “…individuals in a crowd….a crowd of individuals….one or more binary images of connected components corresponding to heads of the individuals of the crowd…training set (TDS) composed of images of crowds of individuals whose heads are annotated….each image of a crowd…a binary image of connected components corresponding to the heads of the individuals.”
The claim language includes multiple instances of a crowd, individuals, image, images, binary images. It is unclear if these instances are different or the same element. It is unclear if “a binary image” is of the one or more binary images.
Claim 14 (and corresponding dependent claim 15) recites …one or more images of a crowd of individuals…..the data processing device as claimed in claim 11 and configured to receive and process one or more images acquired by the acquisition module.
Claim 14 has two instances of one or more images and it is unclear if the images are the same set or two different sets of images. Also, Claim 11 recites “…individuals in a crowd….a crowd of individuals….one or more binary images of connected components corresponding to heads of the individuals of the crowd…training set (TDS) composed of images of crowds of individuals whose heads are annotated….each image of a crowd…binary image of connected components corresponding to the heads of the individuals.” The claim language includes multiple instances of a crowd, individuals, image, images, binary images. It is unclear if these instances are different or the same element.
The Office suggests rewriting the claims in independent form for clarity and consistency.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 4, 10, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Liang et al (“Focal Inverse Distance Transform Maps for Crowd Localization, “ https://arxiv.org/pdf/2102.07925) in view of Hou et al (“BBA-Net: A Bi-Branch Attention Network for Crowd Counting,” cited on IDS filed 09/20/2024, and hereafter referred to as “Hou”).
Regarding Claim 1, Liang discloses a method, implemented by computer, for locating and counting individuals in a crowd, said method takes, as input data, one or more images of a crowd of individuals, and provides, as output data, one or more binary images of connected components corresponding to heads of the individuals of the crowd, said method comprising:
(a) providing a convolutional neural network (CNN) previously trained on a training set (TDS) composed of images of crowds of individuals whose heads are annotated, (Figure 1, § I (see Page 2));
(b) generating, for each image of a crowd supplied as input data, a prediction map (PM) by processing said image through the convolutional neural network (CNN) provided in step (a) (§II, A, predicted map) and
(c) binarizing each prediction map (PM) generated in step (b) using a threshold value (T) or a map of threshold values (TM), each binarized prediction map (BPM) being a binary image of connected components corresponding to the heads of the individuals (§II, A).
Liang does not explicitly disclose the annotations of each image of said training set TDS having previously been modified using a process of tiling as adjacent cells.
Hou discloses a method, implemented by computer, for locating and counting individuals in a crowd, said method takes, as input data, one or more images of a crowd of individuals, and provides, as output data, one or more binary images of connected components corresponding to heads of the individuals of the crowd, said method comprising:
(a) providing a convolutional neural network (CNN) previously trained on a training set (TDS) composed of images of crowds of individuals whose heads are annotated, the annotations of each image of said training set TDS having previously been modified using a process of tiling as adjacent cells (§2.1, §3.3, Figure 3, § 1 );
(b) generating, for each image of a crowd supplied as input data, a prediction map (PM) by processing said image through the convolutional neural network (CNN) provided in step (a) (§2.2, the prediction map is generated by the self- attention block).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Liang to include the missing limitations as taught by Hou in order to increase effectiveness and robustness (§ 1) as disclosed by Hou.
Regarding Claim 2, Liang and Hou disclose all the limitations of Claim 1. Liang discloses step (a), the heads of the individuals of the images of the training set (TDS) are annotated using boxes encompassing said heads (§ I).
Regarding Claim 4 and 19, Liang and Hou disclose all the limitations of Claim 1 and 2 respectively. Hou discloses wherein, in step (a), the tiling is a Voronoi breakdown (§2.1). Same motivation as above.
Regarding Claim 10, Liang and Hou disclose all the limitations of Claim 1. Liang discloses wherein the convolutional neural network (CNN) is an HRNet convolutional neural network (§3,B).
Regarding Claim 12, Liang discloses a non-transitory computer readable medium having stored thereon a computer program having instructions which, when the program is run by a computer, cause the computer to implement the method as claimed in claim 1 (§ V, E, § I methods are performed by GPU device using a CNN which necessitates a computer readable medium with a program ). See rejection of claim 1, same motivation as above.
Claims 11, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Chan et al (US 2024/0144490 and hereafter referred to as “Chan”) in view of Liang and Hou.
Regarding Claim 11, Liang discloses a data processing device comprising:
processing circuitry for locating and counting individuals in a crowd that takes, as input data, one or more images of a crowd of individuals, and provides, as output data, one or more images with pixels of connected components corresponding to heads of the individuals of the crowd (Page 3, paragraph 0038, 0047, Page 4, paragraph 0053), the processing circuitry being configured to:
provide a convolutional neural network (CNN) previously trained on a training set (TDS) composed of images of crowds of individuals whose heads are annotated (Page 4, paragraph 0057, Page 6, paragraph 0070, Page 7, paragraph 0080).
Chan does not explicitly disclose one or more binary images of connected components corresponding to heads of the individuals of the crowd, the annotations of each image of said training set TDS having previously been modified using a process of tiling as adjacent cells; generate, for each image of a crowd supplied as input data, a prediction map (PM) by processing said image through the convolutional neural network (CNN); and binarize each prediction map (PM) using a threshold value (T) or a map of threshold values (TM), each binarized prediction map (BPM) being a binary image of connected components corresponding to the heads of the individuals.
Liang discloses one or more binary images of connected components corresponding to heads of the individuals of the crowd, provide a convolutional neural network (CNN) previously trained on a training set (TDS) composed of images of crowds of individuals whose heads are annotated, (Figure 1, §I (see Page 2)); generate for each image of a crowd supplied as input data, a prediction map (PM) by processing said image through the convolutional neural network (CNN) provided in step (a) (§II, A, predicted map) and binarize each prediction map (PM) generated in step (b) using a threshold value (T) or a map of threshold values (TM), each binarized prediction map (BPM) being a binary image of connected components corresponding to the heads of the individuals (§II, A). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chan to include the missing limitations as taught by Liang in order to improve the structure and achieve state of art localization (§ I) as disclosed by Liang.
Liang does not explicitly disclose the annotations of each image of said training set TDS having previously been modified using a process of tiling as adjacent cells.
Hou discloses a method, implemented by computer, for locating and counting individuals in a crowd, said method takes, as input data, one or more images of a crowd of individuals, and provides, as output data, one or more binary images of connected components corresponding to heads of the individuals of the crowd, said method comprising: providing a convolutional neural network (CNN) previously trained on a training set (TDS) composed of images of crowds of individuals whose heads are annotated, the annotations of each image of said training set TDS having previously been modified using a process of tiling as adjacent cells (§2.1, §3.3, Figure 3, § 1 ); generating, for each image of a crowd supplied as input data, a prediction map (PM) by processing said image through the convolutional neural network (CNN) provided in step (a) (§2.2, the prediction map is generated by the self- attention block).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination to include the missing limitations as taught by Hou in order to increase effectiveness and robustness (§ 1) as disclosed by Hou.
Regarding Claim 14, Chan discloses a system for locating and counting individuals in a crowd, said system comprising: an acquisition module for acquiring one or more images of a crowd of individuals (Figure 1A, 120); and the data processing device as claimed in claim 11 (see rejection of claim 11 above) and configured to receive and process one or more images acquired by the acquisition module (Figure 1A, 130, Figure 1B, 130). See rejection of claim 11 above.
Regarding Claim 15, Chan, Liang and Hou disclose all the limitations of Claim 14. Chan discloses wherein the acquisition module is configured for the acquisition of images of a perspective overhead image of a crowd of individuals (Figure 4).
Allowable Subject Matter
Claims 3, 5-9, 16-18, 20-21 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph and objections, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARZANA HOSSAIN whose telephone number is (571)272-5943. The examiner can normally be reached 9:00 am to 5:00 pm.
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/FARZANA HOSSAIN/Primary Examiner, Art Unit 2482
June 16, 2026
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