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 .
Information Disclosure Statement
The listing of references in the PCT international search report is not considered to be an information disclosure statement (IDS) complying with 37 CFR 1.98. 37 CFR 1.98(a)(2) requires a legible copy of: (1) each foreign patent; (2) each publication or that portion which caused it to be listed; (3) for each cited pending U.S. application, the application specification including claims, and any drawing of the application, or that portion of the application which caused it to be listed including any claims directed to that portion, unless the cited pending U.S. application is stored in the Image File Wrapper (IFW) system; and (4) all other information, or that portion which caused it to be listed. In addition, each IDS must include a list of all patents, publications, applications, or other information submitted for consideration by the Office (see 37 CFR 1.98(a)(1) and (b)), and MPEP § 609.04(a), subsection I. states, “the list ... must be submitted on a separate paper.” Therefore, the references cited in the international search report have not been considered. Applicant is advised that the date of submission of any item of information in the international search report will be the date of submission of the IDS for purposes of determining compliance with the requirements for the IDS with 37 CFR 1.97, including all timing statement requirements of 37 CFR 1.97(e). See MPEP § 609.05(a).
In this situation there are no copies of each foreign patent publication or non-patent literature in the record.
Specification
The disclosure is objected to because of the following informalities: Paragraph 89, “server computing system” is defined in Fig 4A. as 130, but this paragraph uses 140 to refer to it.
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 limitations 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 limitations are: “processing, by the computing system”; “evaluating, by the computing system,”; “modifying, by the computing system”; “generating, by the computing system”; “determining, by the computing system”; “filtering, by the computing system”; and “performing, by the computing system,” in claims 10-15.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they 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 these limitations 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 limitations recite 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 § 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.
Claims 1-2, 4, and 7-16, and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a mental process. This judicial exception is not integrated into a practical application because the recited computer elements do not amount to significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer system recited in claim 1 is generic. It comprises of one or more processors which are used to process instructions, and the computer-readable media is simply storing the instructions of these processes. The recited “machine-learned visual attention center prediction model” is not significantly more as it is a generic recitation of machine learning to tie the invention to a particular technological field. The computing system of claims 10-17 is more generic than the computer system of claims 1-9 where the only structure recited is that the system comprises computing devices which is more generic than the processors or computer readable medium. Claim 1 performs a more generic function than that of claim 10, and it is done via generically recited computer elements where claim 1 is in effect a simpler version of claim 10.
In regards to claim 1, a computer system for prediction of visual attention centers, the computer system comprising: one or more processors (The processors as recited are generic computer components, a person of ordinary skill in the art can predict areas of visual attention by simply observing an object or by observing other people observe an object and make a note of what tends to get the most attention); a machine-learned visual attention center prediction model configured to receive and process an input image to predict a visual attention center for the input image (A person of ordinary skill can receive and observe this in an image); and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computer system to perform operations, the operations comprising: obtaining the input image (A person of ordinary skill can store instruction on how to evaluate or assess the visual attention of an image, and they could further obtain the image by having someone hand it to them); processing the input image with the machine-learned visual attention center prediction model to obtain the visual attention center for the input image (A person of ordinary skill in the art can identify an area or place that is the center of attention in an image); and providing the visual attention center for the input image as an output (A person could output their findings or observations onto a piece of paper using a pen and paper).
In regards to claim 2, wherein: the input image comprises a plurality of pixels (All digital images are comprised of pixels); and the machine-learned visual attention center prediction model is configured to predict a single group of one or more pixels as the visual attention center for the input image (A person of ordinary skill in the art can look at an image and identify a group of pixels that is the visual attention center).
In regards to claim 4, wherein the visual attention center predicted for the input image by the machine-learned visual attention center prediction model comprises a portion of the input image that is predicted to be at a center of human visual attention afforded to the input image over a period of viewing time (A person of ordinary skill in the art can predict the center of attention over a period of time).
In regards to claim 7, wherein: the machine-learned visual attention center prediction model has been trained on a set of training data (A person of ordinary skill in the art can use data to train themselves to identify certain attention centers in an image); the training data comprises a plurality of training examples (The training data, that a person may train on, can be exemplary of various centers of attention); and each training example comprises a training image and a label that indicates a labelled visual attention center for the training image (The examples can be of the image and of the labels on the image that identify the attention centers).
In regards to claim 8, wherein the labelled visual attention center for the training image for each training image has been generated by: obtaining a plurality of attention points for the training image, the plurality of attention points indicating respective locations of human visual attention on the training image (A person of ordinary skill in the art can identify multiple positions indicative of people’s visual attention by observing where a group of people look at on an image); filtering the plurality of attention points to determine a filtered set of attention points (A person of ordinary skill in the art can further filter the points in some manner either by limiting which points to include either temporally or spatially); and determining the labelled visual attention center based on the filtered set of attention points (A person of ordinary skill in the art can further identify a center point by determining a point in the center visually).
In regards to claim 9, wherein filtering the plurality of attention points to determine the filtered set of attention points comprises one or both of: performing temporal filtering to filter out any of the plurality of attention points that correspond to respective locations of human visual attention that occur after a threshold period of viewing time (A person of ordinary skill can filter an image temporally by only keeping positions that maintain a person’s gaze for a certain amount of time over a threshold or via some other method); and performing spatial filtering to filter out any of the plurality of attention points that exist in a region of the training image having a attention point density below a threshold level of density (A person of ordinary skill in the art can perform some form of spatial filtering by only picking areas that are less densely populated by points than a certain threshold).
In regards to claim 10, A computer-implemented method for training a visual attention center prediction model, the method comprising: obtaining, by a computing system comprising one or more computing devices, a set of training data, wherein the training data comprises a plurality of training examples, and wherein each training example comprises a training image and a label that indicates a labelled visual attention center for the training image (A person of ordinary skill in the art can train themselves with training data that comprises an image and the respective labels); accessing, by the computing system, the visual attention center prediction model, wherein the visual attention center prediction model is configured to receive and process an input image to predict a visual attention center for the input image (A person of ordinary skill can receive and observe this in an image); and for each of the plurality of training examples: processing, by the computing system, the training image with the visual attention center prediction model to obtain a predicted visual attention center for the training image (A person of ordinary skill in the art could apply the same processes to a training image as they do to the input image); evaluating, by the computing system, a loss function that compares the predicted visual attention center for the training image to the labelled visual attention center for the training image provided by the label (A person of ordinary skill in the art could perform a loss function of some kind using pencil and paper between the predicted point and the one provided by the labelled image); and modifying, by the computing system, one or more parameters of the visual attention center prediction model based on the loss function (A person of ordinary skill in the art could then modify their predictions to more accurately reflect the labelled data based off of this loss).
In regards to claim 11, wherein: obtaining, by the computing system, the set of training data, comprises generating, by the computing system, the respective label for each training image (A person of ordinary skill in the art can create or label the points of a training image); and for each training image, generating, by the computing system, the respective label comprises: obtaining, by the computing system, a plurality of attention points for the training image (A person can identify the points of visual attention of the image), the plurality of attention points indicating respective locations of human visual attention on the training image (These points would be indicative of where human visual attention is on the image as these points are generated by a human); determining, by the computing system, the labelled visual attention center based on the plurality of attention points (A person can further identify the center of these points by visually observing one point that is more in the center).
In regards to claim 12, wherein determining, by the computing system, the labelled visual attention center based on the plurality of attention points comprises: filtering, by the computing system, the plurality of attention points to determine a filtered set of attention points (A person of ordinary skill in the art can further filter the points in some manner either by limiting which points to include either temporally or spatially); and determining, by the computing system, the labelled visual attention center based on the filtered set of attention points (A person of ordinary skill in the art can further identify a center point by determining a point in the center visually).
In regards to claim 13-14, these claims split the temporal filtering and spatial filtering of claim 9 into two claims. So, these claims are similar to claim 9, and they are similarly rejected.
In regards to claim 15, wherein determining, by the computing system, the labelled visual attention center based on the filtered set of attention points comprises: determining a center of the filtered set of attention points (A person of ordinary skill in the art can determine a central point of the a filtered set of points by simply observing that the point is in the center of other points); and setting the labelled visual attention center equal to the center of the filtered set of attention points (A person could then use that central point as the visual attention center).
In regards to claim 16, it is similar to claim 2, and it is similarly rejected.
In regards to claim 18, it is similar to claim 4, and it is similarly rejected.
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.
(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.
Claims 1-2, 4, 7-12, 14, 16, and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jetley et al. (US 20170308770 A1).
Claims 1-2, 4, 7-12, 14, 16, and 18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Jetley et al. (US 20170308770 A1), hereinafter referred to as Jetley.
In regards to claim 1, Jetley discloses a computer system for prediction of visual attention centers, the computer system comprising: one or more processors (Discloses that visual attention is predicted and placed on a saliency map with paragraph 10 allowing the process to be performed by a processor); a machine-learned visual attention center prediction model configured to receive and process an input image to predict a visual attention center for the input image (Paragraphs 24-25, Discloses the use of a neural network to identify the salient parts of the image and the process is performed on an input image); and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computer system to perform operations, the operations comprising: obtaining the input image (Paragraphs 34 and 50, Paragraph 34 discloses that non-transitory media can contain the programs with paragraph 50 detailing the methods of obtaining the image); processing the input image with the machine-learned visual attention center prediction model to obtain the visual attention center for the input image (Paragraphs 24-25, Discloses the use of a neural network to identify the salient parts of the image and the process is performed on an input image); and providing the visual attention center for the input image as an output (Paragraph 31, Discloses that the output is the saliency map).
In regards to claim 2, Jetley discloses wherein: the input image comprises a plurality of pixels (Paragraph 42, Discloses that the images are made of pixels); and the machine-learned visual attention center prediction model is configured to predict a single group of one or more pixels as the visual attention center for the input image (Paragraph 29, Discloses that the region identified can be a singular one).
In regards to claim 4, Jetley discloses wherein the visual attention center predicted for the input image by the machine-learned visual attention center prediction model comprises a portion of the input image that is predicted to be at a center of human visual attention afforded to the input image over a period of viewing time (Paragraphs 24-25, Discloses the use of a neural network to identify the salient parts of the image and the process is performed on an input image. This process occurs over a period of time, and covers the claimed terms).
In regards to claim 7, Jetley discloses wherein: the machine-learned visual attention center prediction model has been trained on a set of training data (Paragraph 9, Discloses that the system is trained on training data); the training data comprises a plurality of training examples (Paragraph 9, Discloses that multiple images are used which would cover the claim terminology); and each training example comprises a training image and a label that indicates a labelled visual attention center for the training image (Paragraph 9, The disclosed attention maps act as labels for where the saliency or attention is located).
In regards to claim 8, Jetley discloses wherein the labelled visual attention center for the training image for each training image has been generated by: obtaining a plurality of attention points for the training image, the plurality of attention points indicating respective locations of human visual attention on the training image (Paragraph 56, Discloses that the training images are made via humans looking at the image); filtering the plurality of attention points to determine a filtered set of attention points (Paragraph 57, Discloses that Gaussian filtering can be applied to the attention maps); and determining the labelled visual attention center based on the filtered set of attention points (Paragraph 57, Discloses that Gaussian filtering can be applied to the attention maps as this is a post processing technique, these would be used to determine the labelled center).
In regards to claim 9, Jetley discloses and performing spatial filtering to filter out any of the plurality of attention points that exist in a region of the training image having a attention point density below a threshold level of density (Paragraph 87, Discloses that a threshold can be used to filter out certain spatial regions in the saliency map, and the claim only requires either temporal filtering or spatial filtering).
In regards to claim 10, Jetley discloses A computer-implemented method for training a visual attention center prediction model, the method comprising: obtaining, by a computing system comprising one or more computing devices, a set of training data, wherein the training data comprises a plurality of training examples, and wherein each training example comprises a training image and a label that indicates a labelled visual attention center for the training image (Paragraph 9, Discloses that the system is trained on training data where multiple images are used and where disclosed attention maps act as labels for where the saliency or attention is located); accessing, by the computing system, the visual attention center prediction model, wherein the visual attention center prediction model is configured to receive and process an input image to predict a visual attention center for the input image (Paragraphs 24-25, Discloses the use of a neural network to identify the salient parts of the image and the process is performed on an input image); and for each of the plurality of training examples: processing, by the computing system, the training image with the visual attention center prediction model to obtain a predicted visual attention center for the training image (Paragraph 12, Discloses that the neural network can generate the attention maps/saliency maps); evaluating, by the computing system, a loss function that compares the predicted visual attention center for the training image to the labelled visual attention center for the training image provided by the label (Paragraph 22, Discloses the use of loss functions in the training of this model); and modifying, by the computing system, one or more parameters of the visual attention center prediction model based on the loss function (Paragraph 82, Discloses that the weights can be modified in the function in the process of backpropagation).
In regards to claim 11, Jetley discloses wherein: obtaining, by the computing system, the set of training data, comprises generating, by the computing system, the respective label for each training image; and for each training image, generating, by the computing system, the respective label comprises: obtaining, by the computing system, a plurality of attention points for the training image (Paragraph 12, Discloses that the neural network can generate the attention maps/saliency maps for the training images), the plurality of attention points indicating respective locations of human visual attention on the training image (Paragraph 56, Discloses that the training images are made via humans looking at the image); determining, by the computing system, the labelled visual attention center based on the plurality of attention points (Paragraph 12, Discloses that the neural network can generate the attention maps/saliency maps).
In regards to claim 12, Jetley discloses wherein determining, by the computing system, the labelled visual attention center based on the plurality of attention points comprises: filtering, by the computing system, the plurality of attention points to determine a filtered set of attention points (Paragraph 57, Discloses that Gaussian filtering can be applied to the attention maps); and determining, by the computing system, the labelled visual attention center based on the filtered set of attention points (Paragraph 57, Discloses that Gaussian filtering can be applied to the attention maps as this is a post processing technique, these would be used to determine the labelled center).
In regards to claim 14, it is similar to claim 9, and it is similarly rejected.
In regards to claim 16, it is similar to claim 2, and it is similarly rejected.
In regards to claim 18, it is similar to claim 4, and it is similarly rejected.
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.
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.
Claims 3 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Jetley et al. (US 20170308770 A1), hereinafter referred to as Jetley, in view of Gill et al. (US 20210064955 A1), hereinafter referred to as Gill.
In regards to claim 3, Jetley discloses wherein: the input image comprises a plurality of pixels (Paragraph 42, Discloses that the images are made of pixels).
Jetley does not explicitly disclose and the machine-learned visual attention center prediction model is configured to predict a single pixel as the visual attention center for the input image.
However, Gill does disclose and the machine-learned visual attention center prediction model is configured to predict a single pixel as the visual attention center for the input image (Paragraphs 34-35, Discloses that an individual pixel can be just mapped out on an image).
It would be prima facie obvious to combine the teachings of these two arts as it would be simple substitution and obvious to try. Jetley already discloses identifying a group of pixels as a center of human attention. One could simply substitute a group of pixels with a single pixel as disclosed by Gill. Further, it would be obvious to try. An image is comprised of pixels. Thus, any center of visual attention of an image comprised of pixels must be either a group of pixels of interest or of a pixel of interest. As there are only two options, it would be obvious to try both options. As such, it is prima facie obvious to combine.
In regards to claim 17, it is similar to claim 3, and it is similarly rejected.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Jetley et al. (US 20170308770 A1), hereinafter referred to as Jetley, in view of Turlikov et al. (US 20130142446 A1), hereinafter referred to as Turlikov.
In regards to claim 5, Jetley does not explicitly disclose wherein providing the visual attention center for the input image as the output comprises using the visual attention center to perform one or more of image compression, progressive image encoding, or progressive image decoding on the input image.
However, Turlikov does explicitly disclose wherein providing the visual attention center for the input image as the output comprises using the visual attention center to perform one or more of image compression, progressive image encoding, or progressive image decoding on the input image (Paragraphs 18, 21, and Abstract, Discloses that images can be progressively encoded, progressively decoded, or compressed).
It would be prima facie obvious to combine these references as it would lead to a predictable increase in speed of loading times and in accessibility. The methods of progressive encoding and progressive decoding both allow for portions of the image to be shown before the image is fully finished downloading. This allows for a user to be able to view the image more quickly during the downloading process which improves accessibility while image compression allows for more speed during the downloading process as the image is compressed to take up less storage space. As such, it would be prima facie obvious to combine these arts.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Jetley et al. (US 20170308770 A1), hereinafter referred to as Jetley, in view of Her et al. (US 20250005757 A1), hereinafter referred to as Her.
In regards to claim 15, Jetley does not explicitly disclose wherein determining, by the computing system, the labelled visual attention center based on the filtered set of attention points comprises: determining a center of the filtered set of attention points; and setting the labelled visual attention center equal to the center of the filtered set of attention points.
However, Her does disclose wherein determining, by the computing system, the labelled visual attention center based on the filtered set of attention points comprises: determining a center of the filtered set of attention points (Paragraphs 64-67 and 86, Discloses in paragraphs 64-67 that the image is divided into boundary boxes which are then filtered and the center coordinates are determined); and setting the labelled visual attention center equal to the center of the filtered set of attention points (Paragraphs 64-67 and 86, Paragraphs 67 and 86 disclose that the center point of the determined box is set as the feature point).
It would be prima facie obvious to combine the teachings of these two arts. It would be simple substitution as implementing Her’s method of spatially filtering the sections into various boxes could reasonably replace the spatial filtering disclosed by Jetley. As such, it would be prima facie obvious to combine.
Allowable Subject Matter
Claims 19-20 are allowed.
Claim 6 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 13 is not rejected under 35 U.S.C. 102 or 35 U.S.C. 103.
The following is a statement of reasons for the indication of allowable subject matter: The language of claim 6 is included within the language of claim 19. After searching the prior art, the ordering of the plurality of subportions being based upon the visual attention center could not be located. As such, the independent claims that include this terminology are allowed along with their dependent claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CONOR AIDAN O'MALLEY whose telephone number is (571)272-0226. The examiner can normally be reached Monday - Friday 9:00 am. - 5:00 pm. EST.
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/CONOR A O'MALLEY/ Examiner, Art Unit 2675
/GREGORY A MORSE/ Supervisory Patent Examiner, Art Unit 2698
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