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 .
Status of claims: 1-7, 19-21, 23-25 and 27. Claims 8-18, 22 and 26 are withdrawn from examination.
Response to Arguments
Applicant's arguments filed 12/15/2025 have been fully considered but they are not persuasive.
Applicant remarks – (pages 9-10) Applicant argued the lack of teaching to solve the technical problem by the application with its technical significance of the invention in paragraph 0071-0073 of the specification of the instant invention. The claims of the instant inventio would solve such technical problem cited in 0071-0073. Applicant further argued Markovic et al does not solve the above technical problems, nor does it have the features described above, where CAP PAP test is a system designed to improve the visibility of cancer cells and their precancerous lesions, dysplasia, by pretreatment of cells collected from the cervix for microscopic observation. This is not an issue of "a cell aggregate recognition unit that recognizes a cell aggregate in the micrograph for atypia classification based on a cell aggregate in the cellular specimen ", nor is it "an output unit that outputs a class, including an atypia, applicable to a cell belonging to the cell aggregate" as in the present application. Please see the Remarks for further detail.
Examiner’s response – Examiner respectfully disagree. Markovic et al does not need to solve the technical problem as stated in Remarks. Markovic et al only require to teach what is claimed.
Markovic et al teaches the instant invention of disclosed generic support system, method and non-transitory computer readable medium encoded for cervical cancer screening of independent claims 1, 19 and 23. Instant claim language of independent claims 1, 19, and 23 does not disclosed any sort of specialized hardware, equipment, procedures, method, calculation matrix, preparation that would stand out from Markovic et al teaching. Independent claims 1, 19, and 23 disclosed generic hardware such as image acquisition unit, micrograph, cell aggregate recognition unit, output unit area generic and well-known hardware that one skilled in the field would use in the field of cervical or any cancer screen. Even the term “atypia” used in the claims show how generic the whole system and method is, where the system and method are just looking for something different and not necessarily for characteristic of malignant (cancerous).
Markovic et al teaches this instant invention is so simple that it can be carried out by Pap test in column 3 lines 25-53 and Table 3 by technicians reading the stained smear under a microscope with samples obtained from a uterus and smear on a microscope slide comparing to an atypia Table 3. Markovic et al also recognized the use of neural network in column 5 lines 60- column 6 lines 5 to carry out the teaching of the disclosed instant invention, however, using “neural network” is generic as well. Markovic et al teaches the instant invention and the Office Action below mapped out the detail on those teaching. Examiner advise amendment that would set the instant invention apart from Markovic et al with combination such as specialized hardware, narrowed/novel procedure and detail method that would provide a unique way for cervical cancer screening.
Claim Rejections - 35 USC § 101
Applicant’s Claims, see Claims, filed 12/15/2025, with respect to “recording medium” amended to “non-transitory computer readable medium” have been fully considered and are persuasive. The 35 USC 101 rejection has been withdrawn for claims 23-25.
Claim Rejections - 35 USC § 102
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)(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 1-4, 6-7, 19-21, and 23-25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Markovic et al (US 6,143,512).
Claim 1:
Markovic et al (US 6,143,512) anticipated the following subject matter:
A cervical cancer screening support system comprising:
an image acquisition unit that acquires a micrograph of a cellular specimen for cytodiagnosis of a cervix of uterus (column 13 lines 35- column 14 lines 5, specifically column 13 lines 48-60 with use of microscope for cell image analysis (micrograph) to determine cervical cell for detection of abnormality; column 6 line 60-69 teaches using microscopy for scanning stage and imaging; column 1 lines 45-53 teaches analytical process inside cervical cells cytoplasm for diagnostic; column 3 lines 25-33 detail physicians, using a spatula or a brush scrape mucosal cells from the cervix, at the neck of the uterus, and smear them on a microscopic slide for analysis);
a cell aggregate recognition unit that recognizes a cell aggregate in the micrograph for atypia classification based on a cell aggregate in the cellular specimen (column 5 lines 10-25 teaches classification using liquid base smear for diagnosis, where column 5 line 60 – column 6 lines 10 teaches use of neural network to recognize the selected cell/cluster for atypical images for cell classification/specimen; column 13 lines 35- column 14 lines 5); and
an output unit that outputs a class, including an atypia, applicable to a cell belonging to the cell aggregate (column 13 lines 35- column 14 lines 5; column 3 lines 25-53 and atypia Table 3).
Claim 2:
The cervical cancer screening support system according to claim 1, wherein the cell aggregation recognition unit performs LBC on the cellular specimen collected (column 5 lines 5-20; column 5 lines 35-45).
Claim 3:
The cervical cancer screening support system according to claim 1, wherein the output unit outputs a class, including an atypia, by using deep learning to automatically extract a feature depending on the atypia or type from the cell aggregate (column 5 line 60-column 6 lines 5 teaches use of neural network to learn, recognize and generalize).
Claim 4:
The cervical cancer screening support system according to claim 1, wherein an area including the cell aggregate includes a background around the cell aggregate (column 13 lines 40-60 teaches microscope slide for image analysis for stain cellular structure of cell and staining background for classifiable by morphologic criteria).
Claim 6:
The cervical cancer screening support system according to claim 1, wherein the cell aggregate recognition unit recognizes a cell aggregate in real time (column 32 lines 65-column 33 lines 5 teaches TV with real image in real time video; column 34 lines 25-35 teaches microscope slide with TV in real time with cytoplasm for cervical cells).
Claim 7:
A cervical cancer screening support system according to claim 1, further comprising:
an estimation unit that estimates and outputs (column 5 line 60-column 6 line 10 use neural network for output of recognize), when a micrograph acquired by the image acquisition unit is input (column 6 line 60-69 teaches using microscopy for scanning stage and imaging), a position of a cell found to be likely to be abnormal in the micrograph and a class applicable to the cell likely to be abnormal (column 6 lines 60-68 teaches scanning and image analysis with location of cells; column 32 lines 50-60 teaches producing coordinates of location of cells on the slide; above teaches use of microscope for imaging in real time),
by using an estimation model generated through machine learning according to an object detection algorithm, using, as training data, a marked cell aggregate, of cell aggregates recognized by the cell aggregate recognition unit, that includes an atypical cell and an atypia of a cell included in the marked cell aggregate (column 5 line 50 -column 6 lines 7 teaches neural network (estimate model of machine learning) that is able to learn and recognize (training) each cell and cell cluster (object detection) for atypical cell of high grade lesion/inflammatory cell (atypical and atypia cell)).
Claim 19:
Markovic et al (US 6,143,512) anticipated the following subject matter:
A cervical cancer screening support method comprising:
acquiring, by using an image acquisition unit (column 34 lines 25-35 teaches microscope slide/images with TV in real time with cytoplasm for cervical cells), a micrograph of a cellular specimen for cytodiagnosis of a cervix of uterus (column 13 lines 35- column 14 lines 5, specifically column 13 lines 48-60 with use of microscope for cell image analysis (micrograph) to determine cervical cell for detection of abnormality; column 6 line 60-69 teaches using microscopy for scanning stage and imaging; column 1 lines 45-53 teaches analytical process inside cervical cells cytoplasm for diagnostic);
recognizing a cell aggregate in the micrograph for atypia classification based on a cell aggregate in the cellular specimen (column 5 lines 10-25 teaches classification using liquid base smear for diagnosis, where column 5 line 60 – column 6 lines 10 teaches use of neural network to recognize the selected cell/cluster for atypical images for cell classification/specimen; column 13 lines 35- column 14 lines 5); and
outputting a class, including an atypia, applicable to a cell belonging to the cell aggregate (column 13 lines 35- column 14 lines 5).
Claim 20:
The cervical cancer screening support method according to claim 19, further comprising: performing LBC on the cellular specimen collected (column 5 lines 5-20; column 5 lines 35-45).
Claim 21:
The cervical cancer screening support method according to claim 19, wherein the outputting includes outputting a class, including an atypia, by using deep learning to automatically extract a feature depending on the atypia or type from the cell aggregate (column 5 line 60-column 6 lines 5 taches use of neural network (deep learning) to learn, recognize and generalize).
Claim 23:
Markovic et al (US 6,143,512) anticipated the following subject matter:
A recording medium encoded with a cervical cancer screening support program for causing a computer to execute a method comprising:
acquiring, by using an image acquisition unit, a micrograph of a cellular specimen for cytodiagnosis of a cervix of uterus (column 13 lines 35- column 14 lines 5, specifically column 13 lines 48-60 with use of microscope for cell image analysis (micrograph) to determine cervical cell for detection of abnormality; column 6 line 60-69 teaches using microscopy for scanning stage and imaging; column 1 lines 45-53 teaches analytical process inside cervical cells cytoplasm for diagnostic; column 34 lines 25-35 teaches microscope slide/images with TV in real time with cytoplasm for cervical cells);
recognizing a cell aggregate in the micrograph for atypia classification based on a cell aggregate in the cellular specimen (column 5 lines 10-25 teaches classification using liquid base smear for diagnosis, where column 5 line 60 – column 6 lines 10 teaches use of neural network to recognize the selected cell/cluster for atypical images for cell classification/specimen; column 13 lines 35- column 14 lines 5); and
outputting a class, including an atypia, applicable to a cell belonging to the cell aggregate (column 13 lines 35- column 14 lines 5).
Claim 24:
The recording medium according to claim 23 encoded with a cervical cancer screening support program, the method further comprising: performing LBC on the cellular specimen collected (column 5 lines 5-20; column 5 lines 35-45).
Claim 25:
The recording medium according to claim 23 encoded with a cervical cancer screening support program, wherein the outputting outputs a class, including an atypia, by using deep learning to automatically extract a feature depending on the atypia or type from the cell aggregate (column 5 line 60-column 6 lines 5 taches use of neural network (deep learning) to learn, recognize and generalize).
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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Markovic et al (US 6,143,512) in view of Curtin et al (US 2021/0287045).
Claim 5:
Markovic et al teaches all the subject matter above, but the following is taught by Curtin et al:
The cervical cancer screening support system according to claim 1, wherein the cell aggregate recognition unit recognizes a cell aggregate by using YOLO algorithm (0033 teaches classification using neural network including YOLO as object detection model; 0076 and 0084 detail such application to cancer such as cervical).
Markovic et al and Curtin et al are both in the field of image analysis, especially medical image diagnosis using neural/machine/deep network specifically for cervical cancer classification such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Markovic et al by Curtin et al using YOLO where the use of breaking up images into smaller region before computing the class, bounding box update and/or segment mask over the hard to distinguish check of the species as disclosed by Curtin et al in 0033.
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Markovic et al (US 6,143,512) in view of Sethi et al (US 2018/0232883).
Claim 27:
Markovic et al teaches all the subject matter above, but the following is taught by Sethi et al:
A smartphone having a smartphone application installed therein, the smartphone application including the cervical cancer screening support program according to claim 23 (0087 teaches cancer screening for cervical cancer using smartphone with microscope, neural network for disease classification).
Markovic et al and Sethi et al are both in the field of image analysis, especially medical image diagnosis using neural/machine/deep network specifically for cervical cancer classification such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Markovic et al by Sethi et al with the smartphone application for entering patent detail, capture images of the view of microscope with pre-processing module color-normalize the images and pass to POI detection as disclosed by Sethi et al in paragraph 0087.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Boucheron (US 2010/0111396) teaches OBJECT AND SPATIAL LEVEL QUANTITATIVE IMAGE ANALYSIS – 0018 teaches detection of laryngopharyngeal cancer [8], discrimination of cervical cancers and atypias [9], and separation of benign hyperplastic prostatic lesions from true prostatic carcinoma [10]. Additionally Brewer et al. [40] used the red channel from standard RGB light microscopy to classify epithelial and stromal (connective tissue) nuclei in ovarian tissue.
Sethi et al (US 2018/0232883) teaches Systems & Methods For Computational Pathology Using Points-of-interest – 0087 teaches screening for cervical cancer with convolution neural network with classification mode, where use of optical microscope images of tissues (0089).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/TSUNG YIN TSAI/Primary Examiner, Art Unit 2656
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