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 Objections
The abstract of the disclosure is objected to because the abstract is not in the proper forma, i.e. the abstract is not a separate sheet. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 1-25 & 27, specifically independent claim 1, is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Please see the below analysis providing the details as to why the invention is directed towards non-statutory subject matter.
Step 1:
Claim 1 is directed to a method. Therefore, the claim falls within a statutory category of invention.
Step 2A, prong 1:
Claim 1 recites the method steps of:
“…receiving data of an image captured of the plurality of lymphoid cells…”
“…identifying…first pixels corresponding to nuclei…”
“…measuring a value for nuclear size, a value for nuclear intensity…”
“…determining, using the measured values, a classification of leukemia or lymphoma…”
Under the broadest reasonable interpretation, claim 1, recites a series of steps practically performable in the human mind. A human could obtain an image of a lymphoid cells and identify, measure and determine a classification based on data from said image. Therefore, it would be practical to perform the steps in a human’s mind, or with a pen and paper, to determine the claimed classification of leukemia or lymphoma.
Claim 1 recites method steps comprising mental processes (i.e. receiving, identifying, measuring and determining). Therefore, claim 1 recites limitations that fall within the mental processes of abstract ideas, and is directed to an abstract idea.
Step 2A, prong 2:
Claim 1 does not recite additional elements that would integrate the abstract idea into a practical application.
Step 2B
The claims as a whole fail to recite an inventive concept. There are no additional elements that recite significantly more than the abstract idea for the reasons as set forth above in Step 2A, Prong 2.
The examiner also notes that the limitations of dependent claims, i.e. analyzing a plurality of lymphoid cells, measuring a value, determining the classification, further limit the claim limitations already indicated above as being directed to an abstract idea. Therefore, the dependent claims are directed to patient-ineligible subject matter.
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)(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.
Claim(s) 1-2, 8-18, 20-25 & 27-28 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rodellar et al. (‘Image Processing and Machine Learning in Morphological Analysis of Blood Cells’).
Rodellar et al.
1.
A method for analyzing a plurality of lymphoid cells, the method comprising: receiving data of an image captured of the plurality of lymphoid cells in biological biopsy
sample from a subject, the data including pixels,
E.G. via the disclosed image-based system that is able to recognize blood cell, i.e. abnormal lymphoid cells, used to develop of automatic recognition systems (pp. 47, para. 3).
identifying, in the data, first pixels corresponding to nuclei of the plurality of lymphoid cells;
E.G. via the disclosed segmentation of the dividing an image into different parts seen as a color digital image as grid of rectangular pixels (pp. 48, col 1, para 2).
measuring a value for nuclear size, a value for nuclear intensity, and a value for cellular
density using the first pixels;
E.G. via the disclosed feature extraction for identifying perimeter, area and/or shape of nucleus (pp. 48, col 2, para. 3).
and determining, using the measured values, a classification of leukemia or lymphoma in the subject.
E.G. via the disclosed features utilized to provide a classifier to determine a classification of lymphoid neoplasms for clinical pathology diagnosis system [(pp. 49, col 2, para 2-4)-(pp. 50, col 2, para 1)].
2.
The method for analyzing a plurality of lymphoid cells of claim 1, the method further comprising: receiving data of an image captured of the plurality of lymphoid cells in a biological sample from a subject, wherein the data includes pixels;
E.G. via [(pp. 49, col 1, para. 2-3)-(col 2, para. 1-2)].
identifying, in the data, first pixels responding to the plurality of lymphoid cells;
segmenting, using the first pixels, each lymphoid cell of the plurality of lymphoid cells into second pixels corresponding to a first portion with a nucleus and third pixels corresponding to a second portion without a nucleus;
E.G. via the disclosed segmentation technique which identifies regions with pixels having similar value, i.e. establishing a mask per region-of-interest (ROI) (pp. 48, col 1, para. 2-3).
filtering, using the second pixels and the third pixels, the plurality of lymphoid cells to
remove lymphoid cells having overlapping first portions to produce fourth pixels corresponding to a filtered plurality of lymphoid cells;
E.G. via the disclosed segmentation that utilizes a prescribed-threshold (pp. 48, col 1, para. 2-3).
and wherein measuring the value for nuclear size, the value for nuclear intensity, and the value for cellular density using the first pixels comprises measuring the value for nuclear size, the value for nuclear intensity, and the value for cellular density using the fourth pixels corresponding to the filtered plurality of lymphoid cells; and determining, using the measured values, a classification of leukemia or lymphoma in the subject.
E.G. [(pp. 49, col 2, para. 2-4)-(pp. 50, col 2, para 1)].
8.
The method of claim 1, wherein determining the classification
of leukemia or lymphoma comprises determining a level of chronic lymphocytic leukemia (CLL), accelerated CLL, or Richter transformation (RT).
E.G. via the disclosed classifier for the 3 lymphoid neoplasms (pp. 50, col 2, para. 1)
9.
The method of claim 1, wherein the classification is of Hodgkin's lymphoma or non-Hodgkin's lymphoma.
E.G. (pp. 50, col 2, para. 1).
10.
The method of claim 1, wherein measuring the value for nuclear intensity comprises:
measuring subvalues for a plurality of color channels, and determining a statistical value of the subvalues for the plurality of color channels.
E.G. via the disclosed ROI mask for each color component defining the intensity values further utilizing algorithmic tools defined by subsets used to train the classifier [(pp. 49, col 1, para. 1-2) & (Fig 3)].
11.
The method of claim 1, wherein measuring the value for nuclear size comprises:
for each lymphoid cell of the filtered plurality of lymphoid cells:
measuring a cellular value for the nuclear size, comparing the cellular value to a cutoff value, and determining whether the lymphoid cell is in a size classification using the
comparison;
and calculating an amount of lymphoid cells in the size classification.
E.G. via the disclosed geometric features that quantify perimeter, area and shape of the nucleus as defined by a color value, which is further utilized by a mathematically-model classifier [(pp. 48, col 2, para.3-4), (pp. 49, col. 1, para. 4)-(pp. 50, col 2., para.1-3)].
12.
The method of claim 11, wherein the amount of lymphoid cells in the size
classification is a ratio determined using a number of lymphoid cells in the size classification and a number of lymphoid cells not in the size classification.
E.G. via the disclosed segmentation algorithms [(pp. 48, col 1, para. 3)-(pp. 48, col 2., para. 2-3) & (Fig 3)].
13.
The method of claim 11, wherein: the cutoff value is determined using an objective function, and the objective function determined using amounts of lymphoid cell types in a training data set.
E.G. via the disclosed use of a validation subset which utilizes training subsets (e.g., see Fig 3).
14.
The method of claim 1, wherein the plurality of lymphoid cells comprises at least 1,000 lymphoid cells.
E.G. via the disclosed classification matrix using a group of 12 cell images types (e.g., see Fig 5).
15.
The method of claim 1, wherein filtering the plurality of lymphoid cells comprises:
for each lymphoid cell of the plurality of lymphoid cells: determining a ratio of the first portion with the nucleus and the second portion, without the nucleus,
comparing the ratio to a threshold value, and determining the lymphoid cell is in the filtered plurality of lymphoid cells when the ratio exceeds the threshold value.
E.G. [(pp. 49, col 2, para. 2-4)-(pp. 50, col 2, para 1)].
16.
The method of claim 1, wherein: the plurality of lymphoid cells is a first plurality of lymphoid cells, the value for nuclear size is a first value for nuclear size, the value for nuclear intensity is a first value for nuclear intensity, the value for cellular density is a first value for cellular density,
and determining the classification uses a machine learning model trained by:
receiving a plurality of training images, each training image comprising a second
plurality of lymphoid cells, and each training image of the plurality of training images
labeled with a known classification of leukemia or lymphoma;
for each training image of the plurality of training images: measuring a second value for nuclear size, a second value for nuclear intensity, and a second value for cellular density;
optimizing parameters of the machine learning model based on outputs of the
machine learning model matching or not matching the known classification when the
second values are input into the machine learning model, wherein an output of the machine learning model specifies a classification of leukemia or lymphoma for the training image.
E.G. [(pp. 49, col 2, para. 2-4)-(pp. 50, col 2, para 1)].
17.
The method of claim 16, wherein the machine learning model comprises a
A convolutional neural network.
E.G. via the disclosed classification methods utilized with neural networks, decision trees, etc. [(pp. 49, col 2, para. 4) & (Fig 3)].
18.
The method of claim 16, further comprising training the machine learning
model using the plurality of training images.
E.G., [(pp. 49, col 2, para. 4) & (Fig 3)].
20.
The method of claim 1, further comprising: comparing the classification with a previous classification of leukemia or lymphoma in the subject, and determining a progression of the leukemia or lymphoma in the subject based on the comparing.
E.G. [(pp. 49, col 2, para. 2-4)-(pp. 50, col 2, para 1)].
21.
The method of claim 1, wherein the data of the image comprises at least 100,000 pixels.
E.G. via (pp. 48, col 2, para.2).
22.
The method of claim 1, further comprising capturing the image of the plurality of lymphoid cells in the biological sample by performing microscopy on the
biological sample.
E.G. [(pp. 47, col 2, para. 2) & (Fig 1)].
23.
The method of claim 22, further comprising obtaining the biological sample
from the subject.
E.G. [(pp. 47, col 2, para. 2) & (Fig 1)].
24.
The method of claim 1, wherein: the plurality of lymphoid cells is a first plurality of lymphoid cells, and the image comprises a second plurality of lymphoid cells, the method further comprising: selecting a region comprising the first plurality of lymphoid cells as representative of the biological sample from the subject prior to segmenting each lymphoid cell of the first plurality of lymphoid cells.
E.G. [(pp. 49, col 2, para. 2-4)-(pp. 50, col 2, para 1)].
25.
The a method for analyzing a plurality of lymphoid cells of claim 2, the method further comprising: receiving data of an image captured of the plurality of lymphoid cells in a biological sample from a subject, wherein the data includes pixels; identifying, in the data, first pixels corresponding to the plurality of lymphoid cells; segmenting, using the first pixels, each lymphoid cell of the plurality of lymphoid cells into second pixels corresponding to a first portion with a nucleus and third pixels corresponding to a second portion without a nucleus;
filtering, using the second pixels and the third pixels, the plurality of lymphoid cells
to remove lymphoid cells having overlapping first portions to produce fourth pixels
corresponding to a filtered plurality of lymphoid cells; measuring a value for nuclear size and a value for nuclear intensity for each lymphoid cell of the filtered plurality of lymphoid cells using the fourth pixels; determining a value of a parameter using the measured values; comparing the value of the parameter to a reference value; and
wherein determining, using the measure values, the classification of leukemia or
lymphoma in the subject comprises determining, using the comparison, the classification of leukemia or lymphoma in the subject.
E.G. via [(pp. 48, col 1, para. 2-3) & (pp. 49, col 2, para. 2-4)-(pp. 50, col 2, para 1)].
27.
The method for analyzing a plurality of lymphoid cells of claim 1, the method further comprising: receiving data of an image captured of the plurality of lymphoid cells in a biological sample from a subject, wherein the data includes a plurality of pixels; identifying, in the data, first pixels corresponding to the plurality of lymphoid cells, the plurality of pixels comprising the first pixels; segmenting, using the first pixels, each lymphoid cell of the plurality of lymphoid cells into second pixels corresponding to a first portion with a nucleus and third pixels corresponding to a second portion without a nucleus;
filtering, using the second pixels and the third pixels, the plurality of lymphoid cells to remove lymphoid cells having overlapping first portions to produce fourth pixels corresponding to a filtered plurality of lymphoid cells; measuring a value for nuclear size and a value for nuclear intensity for each lymphoid cell of the filtered plurality of lymphoid cells using the first fourth pixels; dividing the data of the image into a plurality of tiles; for each tile of the plurality of tiles: determining a value of a parameter using the measured values of nuclear size and the measured values of nuclear intensity for lymphoid cells corresponding to the first fourth pixels in the tile;
and identifying, using the values of the parameter, a subset of the plurality of tiles as representing cells with increased mitotic activity.
E.G. via [(pp. 48, col 1, para. 2-3) & (pp. 49, col 2, para. 2-4)-(pp. 50, col 2, para 1)].
28.
The method of claim 27, wherein identifying the subset of the plurality of
tiles comprises: for each tile of the plurality of tiles: comparing the value of the parameter with a threshold value, and determining the tile to be in the subset when the value exceeds the threshold value.
E.G. [(pp. 49, col 1, para. 1-2) & (Fig 3)].
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.
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.
Claim(s) 3-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rodellar et al. (‘Image Processing and Machine Learning in Morphological Analysis of Blood Cells’) in view of Huang et al. (US 2014/0023260).
Rodellar et al. discloses a method comprising feature extraction for identifying perimeter, area and/or shape of nucleus (pp. 48, col 2, para. 3), providing the claimed measured value for nuclear size using pixels except wherein the method further comprises measuring a value for distances between cells. Huang et al. teaches that it is known to use a method of segmenting a digital image of biological tissue into biological units, i.e. cells segmented into subsets, wherein said image may contain a plurality of data points used to determine a distance between said points, i.e. in order to determine cell-to-cell distances ([0036]-[0037]).
Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the method as taught by Rodellar et al. with the measuring the cell-to-cell distance as taught by Huang et al. since such a modification would provide the predictable results pertaining to detecting and identifying anomalies, i.e. scale of normal or abnormal units in a digital image representing portions of cells of biological tissue ([0030] & [0035]).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rodellar et al. (‘Image Processing and Machine Learning in Morphological Analysis of Blood Cells’) in view of Wong et al. (US 2006/0127881).
Rodellar et al. discloses a method utilizing data obtained from a pixelated-image in order to determine a classifier for the 3 lymphoid neoplasms, i.e. leukemia or lymphoma (pp. 50, col 2, para. 1), except wherein said method further comprises treating a patient. Wong et al. teaches that it is known to use methods and apparatus provided for automated analysis of images of living cells characterized based on extracted features and classified (e.g., abstract), wherein the initiation of treatment is based on the measure of cell cycle progression [e.g., 0145].
Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the method as taught by Rodellar et al. with the method of providing treatment based on large-scale analysis of cell cycle progression based on the automated analysis of cell images as taught by Wong et al., since such a modification would provide the predictable results pertaining to dissect dynamic cellular processes and to discover mechanisms of action of existing treatment [Wong, 0145].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICOLE F JOHNSON whose telephone number is (571)270-5040. The examiner can normally be reached Monday-Friday 8:00am-5:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Hamaoui can be reached at 571-270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NICOLE F JOHNSON/ Primary Examiner, Art Unit 3796