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 § 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The computing device of claim 1 is directed to a machine, which is one of the statutory categories of invention, and passes Step 1: Statutory Category- MPEP § 2106.03. However, the following limitations of Claim 1 recite steps that can be performed in the human mind or with pen and paper, therefore failing Step 2A Prong One. These limitations constitute mental processes because they describe acts of observation, evaluation, and judgement that can practically be performed in the human mind, or by a human using pen and paper as a physical aid.
wherein the at least one processor is configured to obtain information related to tissues or cells represented in a pathological slide image by analyzing the pathological slide image, predict a ratio of circulating tumor deoxyribonucleic acid (DNA) to cell free DNA, based on the information, and
generate guidance related to a follow-up examination, based on the ratio.
Claim 1 fails Step 2A Prong Two because the additional elements beyond the judicial exception do not integrate the judicial exception into a practical application. The claim does not recite a specific asserted improvement in computer technology (MPEP § 2106.05(a)), and, instead, uses a generic processor and memory to apply the abstract idea on a computer (MPEP § 2106.05(f)). Furthermore, the claim does not impose meaningful limits on the computer components such that the method is tied to a particular machine; the additional elements are described at a high level of generality and can be implemented on any generic computing system (MPEP § 2106.05(b)). Claim 1 also fails Step 2B, as these additional elements are well-understood, routine, and conventional (WURC), adding nothing significantly more than the abstract idea itself (MPEP § 2106.07(a)((III)); a processor and memory are generic computer elements that are WURC (see MPEP § 2106.05(d)). As claims 10 and 20 contain this identical ineligible subject matter, they are also rejected.
Claims 2-9 recite steps that can be performed in the human mind or with pen and paper, therefore failing Step 2A Prong One. These steps constitute mental processes because they describe acts of observation, evaluation, and judgement that a human can practically perform mentally. These claims also fail Step 2A Prong Two and Step 2B because the additional elements beyond the judicial exception, including a processor, do not integrate the judicial exception into a practical application and are WURC (see claim 1 analysis above). The other additional elements beyond the judicial exception, including a machine learning model, also do not integrate the judicial exception into a practical application (see claim 1 analysis above) and are WURC (see Introduction section of He et. al, “A New Method for CTC Images Recognition Based on Machine Learning”). As claims 11-19 contain this identical ineligible subject matter, they are also rejected.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over He et. al (“A New Method for CTC Images Recognition Based on Machine Learning”) in view of Godsey et. al (“Generic Protocols for the Analytical Validation of Next Generation Sequencing-Based ctDNA Assays: A Joint Consensus Recommendation of the Blood PAC’s Analytical Variables Working Group”)
Regarding Claim 1, He teaches a computing system, including at least one processor and memory, that analyzes pathological images of tumor-related cells in order to extract cellular features and derive clinically relevant information. Specifically, He discloses obtaining information related to cells represented in microscopy images through image segmentation and convolutional neural networks, and using the extracted information to support clinical assessment and disease monitoring, including tumor prognosis and therapeutic decision-making, stating that “after segmentation, CNN network were used to identify CTC cells in single nucleus…finally, it enters the output layer and output the result, i.e., CTCs or non-CTCs” (He: The CNN Deep Learning Method Was Used for CTCs Identification). He does not teach predicting a ratio of circulating tumor deoxyribonucleic acid (DNA) to cell free DNA, based on the information, and generating guidance related to a follow-up examination, based on the ratio.
However, Godsey teaches this, stating that “Circulating tumor DNA (ctDNA) is genomic material shed by apoptotic and necrotic tumors into peripheral circulation (1) that typically represents only a small portion of cell-free DNA (cfDNA) present in the blood…” (Introduction), and that “a ctDNA test used for therapeutic response monitoring will need to demonstrate reliable quantitative detection of the absolute number of mutant ctDNA molecules per unit of volume of plasma…” (Introduction). Godsey also states that “guidelines recommend that if a negative ctDNA result is obtained, patients should be reflexed to tissue testing…” (Introduction), and that “ctDNA tests yield information that can help inform treatment decision-making…” (Introduction).
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify He’s system to predict a ratio of circulating tumor deoxyribonucleic acid (DNA) to cell free DNA, based on the information, and generate guidance related to a follow-up examination, based on the ratio, as taught by Godsey, because both references address tumor assessment using circulating tumor biomarkers for clinical decision-making. He motivates automation and machine-learning-based extraction of tumor-related information to reduce subjectivity and improve clinical workflow, while Godsey provides well-established guidance that ctDNA-to-cfDNA ratios are clinically meaningful metrics for determining appropriate follow-up testing. Combining these teachings represents a predictable use of prior-art elements according to their established functions to improve diagnostic reliability and clinical decision support.
Regarding Claim 2, He in view of Godsey teaches the computing device of claim 1, and He further teaches that the information related to the tissues or cells represented in the pathological slide image is obtained by using a first machine learning model, and the first machine learning model is trained to learn a plurality of pathological slide images and pieces of information related to tissues or cells represented in the plurality of pathological slide images (He: Abstract, Materials and Methods, and Figs. 1 and 3 (shown below)).
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Abstract: “Medical image recognition based on machine learning can effectively reduce the workload and improve the level of automation. So, we use machine learning to identify CTCs…we took 2300 cells from 600 patients for training and testing. About 1300 cells were used for training and the others were used for testing.”
The CNN Deep Learning Method Was Used for CTCs Identification: “After segmentation, CNN network were used to identify CTC cells in single nucleus. Finally, it enters the output layer and output the result, i.e., CTCs or non-CTCs.”
Regarding Claim 3, He in view of Godsey teaches the computing device of claim 2, and He further teaches that the information related to the tissues or cells comprises at least one of nuclei sizes, cell density, a cell cluster, cell heterogeneity, spatial distances between cells, and an interaction between cells. (He: Abstract, Materials and Methods, and Figs. 1 and 3 (shown above)).
Abstract: “The images of CTCs were then segmented by image denoising, image filtering, edge detection, image expansion and contraction techniques using python’s openCV scheme.”
The Image Segmentation Method Was Used to Segment Single Nucleus and Give Labels of Cells Instead of Manual: “Nuclei were segmented in the blue channel (DAPI), and the proportion of red in the red channel was detected based on the position of the nucleus.”
Regarding Claim 4, He in view of Godsey teaches the computing device of claim 1, and Godsey further teaches that the ratio of circulating tumor DNA to cell free DNA is predicted using a second machine learning model, and the second machine learning model is trained to learn pieces of information related to tissues or cells represented in a plurality of pathological slide images obtained from a plurality of objects and ratios of circulating tumor DNA to cell free DNA obtained from the plurality of objects. Godsey discloses quantitative measurement and modeling of ctDNA relative to cfDNA, including NGS-based computational analysis pipelines, stating that “circulating tumor DNA (ctDNA) is genomic material shed by apoptotic and necrotic tumors into peripheral circulation (1) that typically represents only a small portion of cell-free DNA (cfDNA) present in the blood…” (Introduction).
Regarding Claim 5, He in view of Godsey teaches the computing device of claim 1, and Godsey further teaches that the processor is further configured to generate guidance related to different follow-up examinations, based on a result of comparing the ratio with at least one threshold value. Godsey discloses decision thresholds and reflex testing based on ctDNA levels, stating that “guidelines recommend that if a negative ctDNA result is obtained, patients should be reflexed to tissue testing…” (Introduction).
Regarding Claim 6, He in view of Godsey teaches the computing device of claim 5, and Godsey further teaches that the processor is further configured to generate first guidance related to a precision genetic analysis examination for a pre-collected blood sample or second guidance related to a precision genetic analysis examination for a pre-collected tissue sample, based on a result of comparing the ratio with a first threshold value, stating that “guidelines recommend that if a negative ctDNA result is obtained, patients should be reflexed to tissue testing…” (Introduction), and that “ctDNA tests yield information that can help inform treatment decision-making…” (Introduction).
Regarding Claim 7, He in view of Godsey teaches the computing device of claim 6, but He does not teach that the processor is further configured to, when the ratio is within a range of less than the first threshold value but a second threshold value or more, generate third guidance related to an additional collection of a blood sample and a precision genetic analysis examination for the pre- collected blood sample and an additionally collected blood sample.
However, Godsey teaches this, stating that “ctDNA is easily obtainable through collection of multiple specimens corresponding to clinically important time points, such as at baseline diagnosis, after surgical resection of a tumor, at progression, or other clinically relevant time points and across the course of different therapeutic regimens….” and “serial monitoring of ctDNA as identified by the presence of a KRAS gene mutation for patients with advanced pancreatic cancer receiving chemotherapy might be useful for early response prediction and therapeutic monitoring” (Introduction).
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Godsey’s teachings into He’s system. Godsey teaches repeated blood collection and serial ctDNA monitoring when initial results are inconclusive or fall within intermediate ranges. Therefore, it would have been obvious to generate guidance for additional blood collection and analysis when the predicted ratio falls within a defined range, as a predictable extension of Godsey’s serial monitoring framework combined with He’s predictive modeling.
Regarding Claim 8, He in view of Godsey teaches the computing device of claim 7, but He does not teach that the processor is further configured to, when the ratio is within a range of less than the second threshold but a third threshold value or more, generate fourth guidance related to the additional collection of the blood sample and the precision genetic analysis examination for the pre-collected tissue sample.
However, Godsey teaches that ctDNA may fail to capture sufficient tumor signal, necessitating tissue-based analysis when plasma results are ambiguous.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Godsey’s teachings into He’s system. Godsey teaches reflexing to tissue analysis when ctDNA results are insufficient or ambiguous, so combining He’s predictive system with Godsey’s reflex-to-tissue guidance when the ratio falls within a particular range would have been obvious in order to improve diagnostic confidence.
Regarding Claim 9, He in view of Godsey teaches the computing device of claim 8, but He does not teach that the processor is further configured to, when the ratio is less than the third threshold value, generate at least one of fifth guidance for additionally collecting the blood sample and recommending a type of precision genetic analysis examination for the pre-collected blood sample and the additionally collected blood sample, and sixth guidance related to the precision genetic analysis examination for the pre-collected tissue sample.
However, Godsey recommends additional testing strategies based on ctDNA sufficiency, stating that a “negative result does not differentiate absence of the mutation of interest from lack of sufficient ctDNA in the sample” (Introduction).
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Godsey’s teachings into He’s system. It would have been obvious to generate different guidance and recommend specific precision genetic analysis types when ctDNA levels fall below a threshold, because Godsey identifies low ctDNA abundance as a known limitation that requires alternative or additional testing strategies to ensure accurate molecular characterization.
Regarding Claim 10, He in view of Godsey teaches all of the limitations of claim 1 above because claim 10 recites a method that performs substantially the same functions as those of the computing device of claim 1.
Regarding Claim 11, He in view of Godsey teaches the method of claim 10, and additional limitations are met as in the consideration of claim 2 above.
Regarding Claim 12, He in view of Godsey teaches the method of claim 11, and additional limitations are met as in the consideration of claim 3 above.
Regarding Claim 13, He in view of Godsey teaches the method of claim 10, and additional limitations are met as in the consideration of claim 4 above.
Regarding Claim 14, He in view of Godsey teaches the method of claim 10, and additional limitations are met as in the consideration of claim 5 above.
Regarding Claim 15, He in view of Godsey teaches the method of claim 14, and additional limitations are met as in the consideration of claim 6 above.
Regarding Claim 16, He in view of Godsey teaches the method of claim 15, and additional limitations are met as in the consideration of claim 7 above.
Regarding Claim 17, He in view of Godsey teaches the method of claim 16, and additional limitations are met as in the consideration of claim 8 above.
Regarding Claim 18, He in view of Godsey teaches the method of claim 17, and additional limitations are met as in the consideration of claim 9 above.
Regarding Claim 19, He in view of Godsey teaches the method of claim 10, and additional limitations are met as in the consideration of claim 1 above.
Regarding Claim 20, He in view of Godsey teaches all of the limitations of claim 1 above because claim 20 recites a method comprising a server and user terminal that performs substantially the same functions as those of the computing device of claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chabon et. al (“Integrating genomic features for non-invasive early lung cancer detection”) teaches a machine-learning based approach for analyzing circulating cell-free DNA from blood samples to estimate the likelihood that a sample contains tumor-derived DNA, including quantifying ctDNA burden relative to background cfDNA. The approach uses thresholded classifier outputs to stratify patients and generate guidance for follow-up clinical evaluation, such as referral for low-dose CT imaging. These teachings are pertinent to the claimed subject matter and would also render the claims obvious when considered as a primary reference in combination with other known techniques.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM ADU-JAMFI whose telephone number is (571)272-9298. The examiner can normally be reached M-T 8:00-6:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached at (571) 270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIAM ADU-JAMFI/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677