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 § 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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-4, 10-13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Pearson Peyton (USPAP 2014/0355,840), in view of Woods (WO 03009209 A1 Computer-aided method and system for detecting spiculated lesions in a mammogram.).
Regarding claim 1 Pearson Peyton teaches, generating, by the breast cancer risk assessment system (Please note, paragraph 0034. As indicated FIG. 1 illustrates an example of a normal mammogram image. In FIG. 1, the pectoralis muscle is shown, along with the adipose tissue and some ducts and glandular tissue. In the example of FIG. 1, a benign lymph node is visible. FIG. 1 illustrates an image of normal, that is, breast cancer free, right and left breasts of a patient.), assessment data including multilevel breast density information generated by measuring multilevel density of an assessment target breast from a mammography image of the assessment target breast. (Please note, paragraph 0036. As indicated FIG. 2 illustrates five examples of breast pairs with varying breast densities. Breast density is a measure for describing the proportion of fibroglandular tissue to fat in the breast. That is, breast tissue density descriptors are based on the proportion of fibroglandular tissue to fat. Breast density is described in the Breast Imaging Reporting and Data Systems (BI-RADS) lexicon as follows: [0037] "Extremely dense" (>75% fibroglandular tissue) [0038] "Heterogeneously dense" (50-75% fibroglandular tissue) [0039] "Scattered fibroglandular densities" (25-50% fibroglandular tissue) [0040] "Almost entirely fatty" (<25% fibroglandular tissue)).
Pearson Peyton does not expressly teach, risk pattern of breast cancer generated by extracting a characteristic pattern of the assessment target breast from the mammography image.
Woods teaches, risk pattern of breast cancer generated by extracting a characteristic pattern of the assessment target breast from the mammography image. (Please note, page 9, paragraph 4. As indicated after the line-based orientation values have been estimated using a line detection algorithm 200, a spiculation detection technique may be performed at step 300 of FIG. 3 A. To identify spiculated lesions, which are a strong indication of malignancy, the current embodiment involves detecting stellate patterns (i.e., areas of locally radiating spicules) in the mammogram. The degree of marginal spiculation is quantified for a neighborhood of pixels according to both the orientation and distance of the pixels surrounding a central pixel. Therefore, more accurate spiculation information can be computed for a range of mass sizes that may have spiculation features located at varying distances from a central mass.).
Pearson Peyton & Woods are combinable because they are from the same field of endeavor.
At the time before the effective filing date, it would have been obvious to a person of ordinary skill in the art to utilize this risk pattern of breast cancer generated by extracting a characteristic pattern of the assessment target breast from the mammography image of Woods in Pearson Peyton’s invention.
The suggestion/motivation for doing so would have been as indicated on page 9, paragraph 4, “more accurate spiculation information can be computed for a range of mass sizes that may have spiculation features located at varying distances from a central mass.”.
Therefore, it would have been obvious to combine Woods with Pearson Peyton to obtain the invention as specified in claim 1.
Regarding claim 2 Pearson Peyton teaches, wherein the generating of the assessment data further includes generating, by the breast cancer risk assessment system, clinical information generated based on at least one of age information, genomic information, and breast cancer family history information of a certain person corresponding to the assessment target breast, and the assessment data includes the clinical information. (Please note, paragraph 0027. As indicated mammogram images may be used for verification for release of prior mammogram images, for example, to medical personnel. In one example, utilizing mammogram image for verification may enable a medical technologist to access and verify prior mammogram images from a storage system (e.g., a cloud storage). Having access to a patient's prior mammogram images may improve the accuracy in interpretation of current mammogram images taken of the patient. Having access to the patient's complete history of mammogram images may help with earlier detection of breast cancer and may reduce the need for additional unnecessary mammogram images to be taken.).
Regarding claim 3 Pearson Peyton teaches, wherein the mammography image is a digital imaging and communications in medicine (DICOM) type image. (Please note, paragraph 0059. As indicated this query, for example, may be performed using DICOM (Digital Imaging and Communications in Medicine) information stored in a cloud system. Based on these queries, mammogram images of potential matches may then be accessed for computer analysis.).
Regarding claim 4 Pearson Peyton teaches, wherein the generating of the assessment data includes measuring density, and the measuring of the density includes preprocessing the mammography image according to a preset contrast and size reference, calculating a size of the assessment target breast by dividing the breast site from the pre-processed mammography image, and calculating breast density by using pixel density of the breast site. (Please note, paragraph 0064. As indicated match the densities at different locations of the breast using a coordinate system (e.g., as illustrated in FIG. 9). For example, assign a rating of 1 to 10 based on the brightness of white as shown in the mammogram image in each grid of the coordinate system. Comparison to a second coordinate system that represents the densities shown in a second mammogram image is then made. If there is a match of the density information of the two coordinate systems, determine that the two mammogram images may belong to the same patient.).
Regarding claims 10-13, similar analysis as those presented for claims 1-4, respectively, are applicable.
Regarding claim 19 Pearson Peyton teaches, a non-transitory computer-readable recording medium
Allowable Subject Matter
Claims 5-9 and 14-18 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 closest applied Prior Art of record fails to disclose or reasonably suggest wherein the calculating of the size of the assessment target breast comprises: dividing the breast site into a first density region, a second density region, and a third density region in descending order of brightness value according to a preset reference of a brightness value for each pixel in the mammography image; and calculating sizes of the first, second, and third density regions of the breast site.
Examiner’s Note
The examiner cites particular figures, paragraphs, columns and line numbers in the references as applied to the claims for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claims, other passages and figures may apply as well.
It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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/AMIR ALAVI/Primary Examiner, Art Unit 2668 Monday, January 19, 2026