DETAILED ACTION
Status of Claims
This action is in reply to the communication filed on 15 April, 2026.
Claims 1, 5 - 10 and 14 - 18 have been amended.
Claims 4 and 13 have been cancelled.
Claim 20 has been added.
Claims 1, 5 – 10 and 14 – 20 are currently pending and have been examined.
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 Interpretation
The claims recite a method and computing apparatus for generating a report related to a secondary clinical trial that includes information on a subset of subjects, selected from a plurality of subjects, based on a subject selection criterion. The subject selection criterion is established by calculating a cut-off value for an expression level corresponding to at least one biomarker among candidate biomarkers, based on survival data/times and quantified biomarker expression levels of subjects of a previously performed, primary clinical trial. The cut-off value of the at least one biomarker is calculated based on a statistical significance of change of survival time – i.e. a preset difference or greater – between survival times before and after the cut-off. Quantified expression levels of biomarkers are generated by applying a generic machine learning model to detect cells and quantified expression levels of the biomarkers.
The specification discloses that “it is significant to select appropriate subjects . . . in order to increase the success rate of clinical trials.” (See the specification as filed @ 0002, 0042) Information related to the secondary clinical trial may be provided in a report that includes information regarding a subject who is a candidate for the (secondary) clinical trial. (@ 0036)
Subjects from a plurality of subjects may be selected for the secondary clinical trial based on the subject having a biomarker expression level above the calculated cut-off. The results from the primary clinical trial are used to set the selection criterion related to responsitivity to the drug (i.e. a statistically significant change in survival time) for selecting subjects. (@ 0043) The criterion is disclosed as a “cut-off value corresponding to the at least one biomarker” used as a criterion for distinguishing responders from non-responders (@ 0065, 0066, 0126) The criterion may be set using a generic machine learning model. (@ 0151, 0161) Here, biomarkers indicate responsitivity based on their expression levels before/after administration of the drug. Subjects who are expected to have a high responsitivity to the drug may be selected. (@ 0131)
Information from the primary clinical trial includes a dataset, stored by a server, of various types of information acquired during the primary clinical trial, and responsitivity results from the drug. The system uses a generic pre-trained machine learning model to analyze pathological slide images in the dataset to identify “biological features” that predict responsitivity of the drug. (@ 0044) Biological features determined from the pathological slide images include “biological elements related to a biomarker” obtained from images taken before administration of the drug and after administration, and compared. (@ 0053, 0067) The system performs an association analysis between the treatment results (i.e. responsitivity such a survival time) of a subject and the biomarkers, such as biomarker expression levels of a genome related to a mechanism of the drug. (@ 0063) A biomarker has a high association with the drug when the expression level of the biomarker shows a great change before and after the administration of the drug (@ 0100, 0142) Nonetheless, the first information and second information described above is acquired from memory. (@ 0062)
As such, the broadest reasonable interpretation of the claims includes generating information regarding a primary clinical trial using a generic machine learning model; and acquiring information indicating drug to biomarker associations – i.e. biomarkers that predict drug responsitivity - from a server memory. The criterion, or cut-off value may be calculated by examining the expression levels for responders and comparing that to non-responders. The information related to the secondary clinical trial includes information for one or more subjects who have an expression level for the biomarker that has been associated with a drug response, where the biomarker has an expression level above (or below) the cut-off value (i.e. a criterion).
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.
The following rejection is formatted in accordance with MPEP 2106.
Claim 10 is representative. Claim 10 recites:
A method of analyzing a biomarker, the method comprising:
generating first information regarding a primary clinical trial previously performed on a certain drug by analyzing, using a machine learning model, pathological slide images of subjects in the primary clinical trial,
wherein the analyzing comprises segmenting the pathological slide images into patch regions, detecting cells in the patch regions, and quantifying expression levels of candidate biomarkers within the cells using the machine learning model, wherein the first information includes the quantified expression levels of the candidate biomarkers, and
the machine learning model is trained to detect the cells and quantify the expression levels of the candidate biomarkers within the cells by adjusting weights of synapses of the machine learning model and reducing an error between a correct output and inferred output corresponding to a particular input;
acquiring second information indicating an association between the drug and each of candidate biomarkers;
determining a least one biomarker among the candidate biomarkers based on the first information and the second information;
generate survival data based on survival times of the subjects in the primary clinical trial and the quantified expression levels of the at least one biomarker from the pathological slide images of the subjects;
establishing a subject selection criterion of the at least one biomarker, by calculating an optimized cut-off value corresponding to the at least one biomarker based on a statistical significance of a change in a survival time when a preset difference or greater appears between survival times before and after the cut-off value; and
generating a report related to a secondary clinical trial based on the subject selection criterion, wherein the report includes information on a subset of subjects selected from a plurality of subjects based on the cut-off value, by analyzing a plurality of pathological slide images of the plurality of subjects and quantifying expression levels of the at least one biomarker of the plurality of pathological slide images using the machine learning model.
Claim 19 recites medium with instructions executed by a processor, and Claim 1 recites an apparatus that executes the steps of the method recited in Claim 10.
Claims 1, 5 – 10 and 14 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept – i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
STEP 1
The claims are directed to an apparatus, a method and non-transitory computer readable medium which are included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims, as illustrated by Claim 10, recite limitations that encompass an abstract idea within the “certain methods of organizing human activity” grouping –
managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions including:
generating first information regarding a primary clinical trial previously performed on a certain drug by analyzing, using pathological slide images of subjects in the primary clinical trial, wherein the first information includes the quantified expression levels of the candidate biomarkers, and
acquiring second information indicating an association between the drug and each of candidate biomarkers;
determining a least one biomarker among the candidate biomarkers based on the first information and the second information;
generate survival data based on survival times of the subjects in the primary clinical trial and the quantified expression levels of the at least one biomarker from the pathological slide images of the subjects;
establishing a subject selection criterion of the at least one biomarker, by calculating an optimized cut-off value corresponding to the at least one biomarker based on a statistical significance of a change in a survival time when a preset difference or greater appears between survival times before and after the cut-off value; and
generating a report related to a secondary clinical trial based on the subject selection criterion, wherein the report includes information on a subset of subjects selected from a plurality of subjects based on the cut-off value.
The claims recite a process for generating a report that includes information on a subset of subjects, from among a plurality of subjects, selected to participate in a secondary clinical trial for a drug, who meet a subject selection criterion – a cut-off value for a particular biomarker having a known association with the drug. First, the claims recite steps that comprise data gathering: generating quantified expression levels of the candidate biomarkers; acquiring second information; determining a least one biomarker; generate survival data; establishing a subject selection criterion. The claims select a subset of subjects who meet a subject selection criteria by comparing the established subject selection criteria, which is a cut-off value for a biomarker expression level, to each subjects measured biomarker expression level. This series of steps comprises an abstract filtering process – selecting information for processing. (See MPEP 2106.04(a)(2) II C). The specification discloses that selecting appropriate subjects for a clinical trial is significant to the clinical trial’s success. Indeed, this type of activity, i.e. selecting subjects for a clinical trial, includes conduct that would normally occur when designing such a trial. For example, it is routine for a clinical trial design to specify appropriate participants using exclusion and/or inclusion criteria. As such, the claims recite an abstract idea within the certain methods of organizing human activity grouping.
The claims, as illustrated by Claim 10, also recite limitations that encompass an abstract idea within the “mental processes” grouping – concepts performed in the human mind including observation, evaluation, judgment and opinion. The claims recite collecting and analyzing information to obtain a result, which is an ordinary mental process.
Collecting information, including when limited to particular content, is within the realm of abstract ideas, and analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, are mental processes within the abstract idea category (Electric Power Group v. Alstom S.A. (Fed Cir, 2015-1778, 8/1/2016).
The first information is acquired by analyzing pathological slides, and the second information is obtained from a memory. Determining a biomarker among the candidates is disclosed as selecting a biomarker that distinguishes between a responder and a non-responder – that is a biomarker that shows a significant change after application of the drug of interest distinguished responders from non-responders. Establishing a subject selection criterion, or cut-off value, is disclosed as observing the difference in responsitivity above and below the cut-off value, for example using survival time as observed (@ 0122). Similarly generating a report related to a secondary trial is disclosed as selecting patients having a measured value for the selected biomarker compared with the subject selection criterion/cut-off/threshold. For example, patients may be selected who have a “TIL density” greater than or equal to the cut-off value. (@ 0126) Such comparisons, observations and judgments, are processes that, except for generic computer implementation steps, can be performed in the human mind. As such, the claims recite an abstract idea within the mental process grouping.
The claims, as illustrated by Claim 10, also recite limitations that encompass an abstract idea within the “mathematical formula or relationship grouping including:
establishing a subject selection criterion of the at least one biomarker, by calculating an optimized cut-off value corresponding to the at least one biomarker based on a statistical significance of a change in a survival time when a preset difference or greater appears between survival times before and after the cut-off value.
Calculating a cut-off value is disclosed as weight determined during regression analysis (0110), which is a mathematical relationship. As such, the claims recite an abstract idea within the mathematical formula or relationship grouping.
STEP 2A PRONG TWO and STEP 2B
The claims recite additional elements beyond those that encompass the abstract idea above including:
using a machine learning model for analyzing pathological slide images, wherein the analyzing comprises segmenting the pathological slide images into patch regions, detecting cells in the patch regions, and quantifying expression levels of candidate biomarkers within the cells using the machine learning model;
the machine learning model is trained to detect the cells and quantify the expression levels of the candidate biomarkers within the cells by adjusting weights of synapses of the machine learning model and reducing an error between a correct output and inferred output corresponding to a particular input.
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with the MPEP. (see MPEP 2106.05).
In particular, the claims apply established methods of machine learning to an abstract filtering process in a new data environment – i.e. applying a trained model to the pathological slide images. The specification broadly discloses the “machine learning-based analysis” or “AI-based biomarker analysis system”. There is no recitation of a particular kind of machine learning model, such as a neural network, random forest, decision tree, etc. Training the model is also generically disclosed and claimed. Machine learning limitations reciting broad, functionally described, well-known techniques executed by generic and conventional computing devices, as in the pending claims here, does not provide a practical application of, or an inventive concept to, the abstract diagnostic process. “Today we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under §101.” (Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025)).
Claim 1 and 19 recite additional structural elements or combination of elements, other than the abstract idea per se, that amounts to no more than a recitation of generic computer structure (i.e. a computing apparatus/processor and memory, computer-readable medium). Each of the above components are disclosed in the specification as being purely conventional and/or known in the industry. Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting well-understood, routine and conventional computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently well-known that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination the limitations recited in the claims add nothing that is not already present when the steps are considered individually. As such, the additional elements recited in the claim do not integrate the abstract idea into a practical application, nor do they provide significantly more than the abstract participant selection process, or an inventive concept.
The dependent claims add additional features including:
those that merely serve to further narrow the abstract idea above such as:
using the biomarker/cut-off as criterion to distinguish responders and non-responders (Claims 5, 14);
further limiting the type of information related to the secondary trial (Claims 6, 15, 20);
further limiting the type of information related to the biomarker (Claims 7, 16);
those that recite additional abstract ideas such as:
collecting first and second slide images and set the criteria based on a change between them (Claims 8, 17);
those that recite well-understood, routine and conventional activity or computer functions such as:
outputting a report (Claims 9, 18);
those that recite insignificant extra-solution activities;
or those that are an ancillary part of the abstract idea.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. These elements merely narrow the abstract idea, recite additional abstract ideas, or append conventional activity to the abstract process. As such, the additional element do not integrate the abstract idea into a practical application, or provide an inventive concept that transforms the claims into a patent eligible invention.
The apparatus claims are no different from the method claims in substance. “The equivalence of the method, system and media claims is readily apparent.” “The only difference between the claims is the form in which they were drafted.” (Bancorp). The method claims recite the abstract idea implemented on a generic computer, while the apparatus claims recite generic computer components configured to implement the same idea. Specifically, Claims 1, 5 – 9 and 19 merely add the generic hardware noted above that nearly every computer will include. The apparatus claim’s requirement that the same method be performed with a programmed computer does not alter the method’s patentability under U.S.C. 101 (In re Grams). Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
The Prior Art
The prior art teaches generating information regarding a primary clinical trial, and generating a report related to a secondary clinical trial based on subject selection criteria established according to results from the primary clinical trial, including information on a subset of subjects selected from a plurality of subjects based on an analysis of treated and untreated biomarker expression values. (Polidori et al.) The prior art further teaches correlating gene expression levels with statistically significant measures of survival time, (Mock et al.); and using machine learning techniques to analyze pathological slide images, by sub-sampling the images into sectors (i.e. patch) to detect cells and quantify biomarker expression levels therein (Van Leeuwen et al.). Training such a machine learning model inherently includes repeatedly adjusting the weights of synapses of the machine learning model and reducing an error between a correct output and an inferred output correspo0nding to a particular input.
Nonetheless, the prior art does not disclose or suggest:
establish a subject selection criterion of the at least one biomarker by calculating an optimized-cut-off value corresponding to the at least one biomarker based on a statistical significance of a change in a survival time when a preset difference or greater appears between survival times before and after the cut-off value.
Response to Arguments
Applicant's arguments filed 15 April, 2026 have been fully considered but they are not persuasive.
The §101 Rejection
Applicant asserts that the claims recite patent-eligible subject matter because it is a “technical solution” – “it quantifies biomarker expression by segmenting and analyzing at the patch level and optimizing machine learning model weights – tasks a human cannot perform”. (REMARKS @ 9)
Initially, Examiner agrees that a human cannot perform these functions, noting that this has not been alleged. Nonetheless, certain limitations can be performed mentally, under the broadest reasonable interpretation of those features. Determining a biomarker among the candidates is disclosed as selecting a biomarker that distinguishes between a responder and a non-responder – that is a biomarker that shows a significant change after application of the drug of interest distinguished responders from non-responders. Establishing a subject selection criterion, or cut-off value, is disclosed as observing the difference in responsitivity above and below the cut-off value, for example using survival time as observed (@ 0122). Similarly generating a report related to a secondary trial is disclosed as selecting patients having a measured value for the selected biomarker compared with the subject selection criterion/cut-off/threshold. For example, patients may be selected who have a “TIL density” greater than or equal to the cut-off value. (@ 0126) Such comparisons, observations, selections and judgments, are processes that, except for generic computer implementation steps, can be performed in the human mind.
Applicant asserts that “generating new indicators, such as quantitive values and survival data, from raw pathological images through an algorithm” transforms abstract concepts into specific physical data. Initially, Examiner notes that survival data is based on survival times; and while survival data may be acquired by analyzing slide images, survival time is measured – not from the slide image itself. Data is not physical, but is abstract.
Applicant asserts calculations that statistically and automatically determining cut-off values where survival increases is a specific practical application that increases the success rate of the clinical trial. Examiner notes that the term “success” is not defined by the specification. The National Institute of Health defines a clinical trial success rate as the percentage of trials that result in “a new drug registration”. As such, improving the success rate, as described here, is not a technological problem “that is necessarily rooted in the computer technology itself.” The problem of selecting appropriate candidates for a clinical trial is a business problem, even though the proposed solution uses computer technology as a tool.
Applicant asserts that the claims “solve a real problem with a claimed solution that improves the analyzing a biomarker, as well as, the success rate of clinical trials, and that is necessarily rooted in the computer technology itself.”
No computer function created the problem related to selecting candidates for a clinical trial by comparing a candidate’s biomarker value to a selection criteria; and no computer functionality is improved here. Further the purported improving the analysis of a biomarker merely improves the abstract idea itself. It is unclear to the Examiner, and the Applicant does not explain, how the problem of selecting clinical trial candidates is “necessarily rooted in the computer technology itself.”
Applicant further asserts that the claims provide for “generating information for assisting design of the secondary clinical trial” based on the results of the primary clinical trial. But designing a clinical trial is an age old human activity, and assisting in that design merely organizes that human activity; improving, not some technological process, but the abstract idea itself. Applicant also asserts that the claims “improve the success rate of clinical trials”; however, this is not commensurate with the scope of the claims. The secondary trial information that is generated is not applied to a secondary trial to realize such an improvement in success rate. No information is given that discloses how such an improvement would be determined. Further, when clinical trials have a low success rate because participants were not properly screened for inclusion or exclusion criteria, it is not because of some technological process that is in need of improvement. Selecting information for analysis is abstract.
Applicant asserts that the claims “solve a real problem . . . that is necessarily rooted in the computer technology itself”, as in DDR Holdings. The problem - “selecting appropriate subjects to participate in a clinical trial” – is not a technological problem, nor does the Applicant point to any place in the specification that describes such a problem. The claims gather information about a primary clinical trial using known techniques and determine a biomarker and cut-off value that secondary trial participants should have.
CONCLUSION
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PGPUB 2019/0286790 A1 to Kaigala et al. discloses a system and method for biomarker qualification in a tissue sample including predicting a survival time using a trained machine learning classifier.
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.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to John A. Pauls whose telephone number is (571) 270-5557. The Examiner can normally be reached on Mon. - Fri. 8:00 - 5:00 Eastern. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773.
Official replies to this Office action may now be submitted electronically by registered users of the EFS-Web system. Information on EFS-Web tools is available on the Internet at: http://www.uspto.gov/patents/process/file/efs/guidance/index.jsp. An EFS-Web Quick-Start Guide is available at: http://www.uspto.gov/ebc/portal/efs/quick-start.pdf.
Alternatively, official replies to this Office action may still be submitted by any one of fax, mail, or hand delivery. Faxed replies should be directed to the central fax at (571) 273-8300. Mailed replies should be addressed to “Commissioner for Patents, PO Box 1450, Alexandria, VA 22313-1450.” Hand delivered replies should be delivered to the “Customer Service Window, Randolph Building, 401 Dulany Street, Alexandria, VA 22314.”
/JOHN A PAULS/Primary Examiner, Art Unit 3683
Date: 1 June, 2026
Prosecution Insights