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 .
Response to Amendment
This Office action is in response to reply filed 4/8/2026. Claims 5 and 14 are canceled and claims 1-4, 6-13, and 15-20 have been amended and are currently pending in the application.
Response to Arguments
Applicant's arguments filed 4/8/2026 with respect to the rejection under 35 U.S.C. § 101 have been fully considered but they are not persuasive.
The examiner respectfully disagrees with the applicant’s argument that ” the human mind is not equipped to obtain information related to tissues or cells”. The obtaining information related to tissue or cells in the claim only requires analyzing a pathological slide image. This can be done by a physician viewing the slide image and predicting the ratio of tumor DNA to cell-free DNA based on the elements in the slide image. The physician would then also generate guidance for follow-up examination based on the ratio and a physician known threshold for the ratio.
The examiner respectfully disagrees with the applicant’s argument that “operations recited in independent claim 1 include meaningful limitations that add more than generally linking the use of an abstract idea to a particular technological environment because they solve a real problem with a solution that improves the predict the tumor content from a stage of a biopsy. These limitations provide unconventional steps that integrate claim 1 to a practical application, and improve the predicting of tumor content from a biopsy.” The only elements beyond the judicial exception in the claim are the generic memory and processor. Thus, the claim does not amount to more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Applicant’s arguments with respect to the rejection under 35 U.S.C. § 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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, 6-10, and 15-20 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 which is obtained from the pathological slide image, and
generate guidance indicating at least one of a follow-up examination, based on a result of comparing the ratio with at least one threshold value.
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 6-9 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 15-19 contain this identical ineligible subject matter, they are also rejected.
Claims 2-4 and 11-13 include details specific to training a machine learning model, which amounts to significantly more than the judicial exception under Step 2B
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 1-4, 6-13, and 15-20 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 Madison et al. (US 2025/0283167).
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 predict a ratio of circulating tumor deoxyribonucleic acid (DNA) to cell free DNA, based on the information which is obtained from the pathological slide image, and generate guidance related to indicating at least one of a follow-up examination, based on a result of comparing the ratio with at least one threshold value.
However, Madison, [0015], [0025], and [0027], teaches that a tumor fraction, which is a ratio of tumor DNA to cell free DNA, is determined, or predicted, from the slide image and that the tumor fraction is compared to threshold to determine appropriate follow-up examination such as adjusted therapy or additional genomic profiling assay.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art for He’s system to include the ration prediction and guidance generation, as taught by Madison. The motivation is to determine needed adjustments to therapy or additional needed analysis to ensure proper treatment is provided for the patient (Madison, [0027]).
Regarding Claim 2, He in view of Madison teaches the computing device of claim 1, and He further teaches obtain, using a first machine learning model, the information related to the tissues or the cells represented in the pathological slide image, and train the first machine learning model to learn a plurality of reference pathological slide images and pieces of reference information related to the tissues or the cells represented in a 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 Madison teaches the computing device of claim 2, and He further teaches that the information related to the tissues or the cells comprises at least one of nuclei sizes, a cell density, a cell cluster, a cell heterogeneity, spatial distances between the cells, or an interaction between the 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 Madison teaches the computing device of claim 1, and Madison further teaches predict, using a second machine learning model, the ratio of the circulating tumor DNA to the cell free DNA, and train the second machine learning model to learn pieces of reference information related to the tissues or the 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 (Madison, [0032]).
Regarding Claim 6, He in view of Madison teaches the computing device of claim 1, and Godsey further teaches that the processor is further configured to:
generate first guidance indicating a precision genetic analysis examination for a pre-collected blood sample or second guidance indicating a precision genetic analysis examination for a pre-collected tissue sample, based on the result of the comparing of the ratio with a first threshold value (Madison, [0027]: The guidance in response to the ratio meting any level may include performing genomic profiling assay for a blood or tissue sample).
Regarding Claim 7, He in view of Madison teaches the computing device of claim 6, but He does not teach that the processor is further configured to:
based on the ratio being less than the first threshold value and greater than or equal to a second threshold value, generate third guidance indicating 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 (Madison, [0027]: The guidance in response to the ratio meeting any level may include performing genomic profiling assay for a blood or tissue sample).
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:
based on the ratio is being less than the second threshold value and greater than or equal to a third threshold value, generate fourth guidance indicating the additional collection of the blood sample and the precision genetic analysis examination for the pre-collected tissue sample (Madison, [0027]: The guidance in response to the ratio meeting any level may include performing genomic profiling assay for a blood or tissue sample).
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:
based on the ratio being less than the third threshold value, generate at least one of fifth guidance indicating 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, or sixth guidance indicating the precision genetic analysis examination for the pre-collected tissue sample (Madison, [0027]: The guidance in response to the ratio meeting any level may include performing genomic profiling assay for a blood or tissue sample).
Regarding Claim 10, He in view of Madison 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 Madison 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 Madison 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 Madison teaches the method of claim 10, and additional limitations are met as in the consideration of claim 4 above.
Regarding Claim 15, He in view of Madison teaches the method of claim 10, and additional limitations are met as in the consideration of claim 6 above.
Regarding Claim 16, He in view of Madison 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 Madison 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 Madison 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 Madison 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 Madison 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. Additional references related to determining a ration of tumor DNA to cell-free DNA are cited in the PTO-892 form.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW W BEE whose telephone number is (571)270-5183. The examiner can normally be reached 9:00 - 7:00 M-Th.
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/ANDREW W BEE/ Supervisory Patent Examiner, Art Unit 2677
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