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 Objections
Claim 4 is objected to because of the following informalities: in line 2, “were” should be changed to --where--; and in line 9, --is configured-- should be inserted after “component”. Appropriate correction is required.
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.
Claims 1-12, 16-20 are rejected under 35 U.S.C. 102a1 as being anticipated by the NPL titled “Mammogram Classification in Transform Domain” to Samant and Sonar, hereinafter referred to as “Samant et al.”
With regard to claim 1, Samant et al discloses a system that is implemented, at least in part, by hardware, comprising: a transform component configured to apply a two-dimensional discrete cosine transform upon an area of interest of a spatial image to transform the area of interest of the spatial image into an area of interest of a frequency domain image (see section ROI extraction and Discrete Cosine Transform (DCT) at page 57); and an identification component configured to identify a sub-area of interest from the area of interest of the frequency domain image through employment of a threshold (See Fig.3 and the DCT algorithm (bottom left of page 58) at steps 3 and 4 with the claimed “threshold” being the dividing line between “Most Important” and “Semi-Important” DCT coefficients in Fig.4).
With regard to claim 2, Samant et al discloses the system of claim 1, comprising: an inversion component configured to take an inverse of the sub-area of interest from the area of interest of the frequency domain image (at step 5 of the DCT algorithm, GLCM textures features are extracted, which inherently involves an inverse DCT in order to transform the previous frequency domain data at step 4 into spatial domain data in step 5. In other words, by definition, a GLCM uses spatial data of the pixels and their corresponding gray-level, see page 59 and “Gray-Level Co-occurrence Matrices”).
With regard to claim 3, Samant et al discloses the system of claim 2, comprising: an intensity component configured to determine an intensity of the sub-area of interest through employment of the inverse of the sub-area of interest from the area of interest of the frequency domain image (See claim 2 above, with intensities of gray-levels).
With regard to claim 4, Samant et al discloses the system of claim 3, were the spatial image is a first spatial image, where the frequency domain image is a first frequency domain image, where the inverse is a first inverse, where the intensity is a first intensity (see claim 3 above), where the transform component is configured to apply the two-dimensional discrete cosine transform upon an area of interest of a second spatial image to transform the area of interest of the second spatial image into an area of interest of a second frequency domain image; where the identification component to identify a sub-area of interest from the area of interest of the second frequency domain image through employment of a threshold, where the inversion component is configured to take an inverse of the sub-area of interest from the area of interest of the second frequency domain image (multiple images of mammograms from the same patient (“case samples” at top left of page 60) are processed), where the intensity component is configured to determine a second intensity of the sub-area of interest through employment of the inverse of the sub-area of interest from the area of interest of the second frequency domain image, where the first spatial image and the second spatial image capture about the same location (mammogram images of same patient), where the area of interest of the first spatial image and the area of interest of the second spatial image cover about the same portion of the location, and where the sub-area of interest from the area of interest of the first frequency domain image and the sub-area of interest from the area of interest of the second frequency domain image cover about the same sub-portion of the location (mammogram of the same patient, with the upper left of Fig 4 being the sub area of interest).
With regard to claim 5, Samant et al discloses the system of claim 4, comprising: a comparison component configured to compare the first intensity to the second intensity to produce a comparison result; and a classification component configured to make a classification of the sub-area of interest based, at least in part, on the comparison result (see pages 59-60 and the section “Mammogram Classification” where a KNN classifier compares (similarity comparison) the sub-areas of multiple images in order to determine classification as either Benign, Malignant, or Normal).
With regard to claims 6 and 7, as broadly recited the claimed “profile” is considered the training dataset that the case sample is compared to, see top of page 60).
With regard to claim 8, Samant et al discloses the system of claim 1, where the sub-area of interest is a single pixel (as broadly claimed, each pixel within the “Most Important” section of Fig. 4 can be considered as a single pixel of the sub-area).
With regard to claim 9, see claims 5 and 6 above.
With regard to claim 10, see claim 2 above.
With regard to claim 11, see claim 1 above.
With regard to claim 12, see claims 1 and 4 above.
Claims 16-20 are rejected for reasoning, mutatis mutandis, as that of claims 1-8 above. Furthermore, with regard to the claimed non-transitory CRM and processor, see Samant et al at Section III: Proposed Methodology, “database.”
Claim Rejections - 35 USC § 103
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 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Samant et al in view of US 2006/0062455 to Chiu et al.
Samant et al discloses an ROI and sub-ROI image analysis process that results in the classification of mammogram images as either benign, malignant or normal. And while Samant et al disclose the single pixel by pixel evaluation of the images, Samant et al fails to disclose the claimed area of interest is no larger than twenty-five pixels by twenty-five pixels and where classifying the sub-area is a binary classification being positive or negative in matching a desired feature.
However, in the same field of endeavor as Samant et al (recognition of ROIs), Chiu et al discloses at [0029]-[0037] that sub-blocks of the image are 16x16 pixels through which each sub-block is subjected to a DCT, and subsequently each sub-block is binarized as either a 1 or 0 (black or white) dependent upon a desired feature, that feature being the standard deviation, see [0037] in particular.
Therefore, it would have been obvious before the effective filing date of the claimed invention to have limited the sub-blocks of Samant et al to less than 25x25 pixels as taught by Chiu et al as doing so would increase the detail and accuracy of the sub-ROI but also impact the processing power of the system as taught by Chiu et al at [0029].
Furthermore, the binarization would also have been obvious to one having ordinary skill in the art of image analysis in view of the teachings of Chiu et al in order to group like (i.e. “1” - black) sub-blocks together to form multiple sub-ROIs of the image as shown in Figure 5 of Chiu et al.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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DAVID OMETZ
Primary Examiner
Art Unit 2672
/DAVID OMETZ/Primary Examiner, Art Unit 2672
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