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 .
Receipt of Applicant’s Amendment filed April 8, 2026, is acknowledged.
Response to Amendment
Claims 40 and 49 have been amended. Claims 40-57 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments with respect to claims 40-57 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. A response is provided below in bold where appropriate.
Applicant addresses Drawing Objection, pg. 7 of Remarks:
Drawing Objections
Replacement Drawings have been submitted herewith. Applicant therefore respectfully requests withdrawal of the objections to the drawings.
Entered and the objection is withdrawn.
Applicant argues 35 USC §101 Rejection, starting pg. 7 of Remarks:
Claim Rejections - 35 U.S.C. § 101
The Examiner has alleged that claims 40-57 are directed to an abstract idea without significantly more. Office Action, page 3. Applicant respectfully traverses this rejection for the following reasons.
With respect to Step 2A, Prong 1, the claims are not directed to an abstract idea. Claims 40 and 49 recite "outputting, by a deep learning model, a covariate for adjustment, the deep learning model trained on histopathological slides obtained from cancer patients, and the covariate comprising a prognostic covariate for a cancer patient based on the histopathological slides." This is not a mental process that can be performed in the human mind or with pen and paper. A deep learning model trained on histopathological slides to output prognostic covariates requires computational processing that cannot be practically performed mentally. Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRI Int'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) (declining to identify the claimed collection and analysis of network data as abstract because "the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims"). The Examiner characterizes the claims as "following rules or instructions" and "teaching." However, claims 40 and 49 specifically recite technical operations including using a deep learning model to process histopathological slides and output prognostic covariates, which are not mere rule-following or teaching activities. As described in the specification, the methods of increasing statistical power of studies and reducing number of events required for a targeted power "incorporat[e] novel sources of prognostic signal obtained using deep learning prognostic models as adjustment covariates" where "[t]he deep learning prognostic models can be based on histopathological slides of cancer patients." As-Filed Specification, page 20, Lines 15-20. This represents a specific technical implementation, not an abstract concept.
The Examiner respectfully disagrees with the above. A person can with pen and paper perform a covariate adjustment based on slides. Even if this were not possible, the trained deep learning model is recited at a high level of generality.
Also, using a deep learning model is using some type of existing unknown generic model at a very high level of generality. This is not improving deep learning models and amounts to a model somehow providing a covariate adjustment.
With respect to Step 2A, Prong 2, the claims are integrated into a practical application. Claims 40 and 49 as amended recite "outputting an evaluation of a specific treatment effect for the cancer patients by conducting a randomized controlled trial (RCT)." This provides a practical application of the claimed method in the context of clinical trial design and execution. The specification explains that the methods disclosed "can incorporate novel sources of prognostic signals, e.g., prognostic scores obtained by deep learning, as adjustment covariates to compute the adjusted (e.g., reduced) sample size" and that "such covariate adjustment methods using deep learning prognostic covariates can improve the statistical power achieved based on the same sample size, and can reduce sample" size needed to achieve the same statistical power. As-Filed Specification, page 11, Lines 5-10. Claims 40 and 49 recite "adjusting the prognostic covariate based on a number of events for a time-to-event outcome identified for the cancer patients." This adjustment step, combined with the deep learning model output and the RCT evaluation, provides a specific technical improvement to clinical trial methodology. The specification further explains that "[r]educing the sample size used for a randomized clinical trial (or reestimating the sample size in a blinded manner) improves the functioning of a device that is used to compile the results of the randomized clinical trial" because "a smaller number of participants are needed to achieve the same statistical power in the randomized clinical trial" and "by having a smaller number of participants, the randomized clinical trial compiles a smaller amount of" data. As- Filed Specification, page 11, Lines 19-24. This "improves the efficiency of the device that is compiling, analyzing, storing, and/or otherwise processing this data as there is a smaller amount of data resulting from a randomized clinical trial with a smaller sample size" and "improves on the efficiency of the device by reducing the computing and storing load on that device." As-Filed Specification, page 11, Lines 25-29. The claims are directed to a practical application of treatment effect estimation in randomized controlled trials, not to an abstract idea.
Conducting a randomized controlled trial is itself abstract as it is managing personal behavior by following rules and instructions. This does not amount to a practical application, which cannot itself be abstract.
The argument regarding covariate improves statistical power based on the same sample size would be abstract as a mathematical concept, had it been claimed.
The above reducing the sample size improves the function of a device used to compute results because of smaller amount of data is an effect or result of a judicial exception. Also, there are no steps that guarantee a benefit and the computer itself is not improved.
With respect to Step 2B, the claims recite significantly more than the alleged abstract idea. The specific combination of elements in claims 40 and 49-using a deep learning model trained on histopathological slides to output prognostic covariates, adjusting those covariates based on time-to-event outcomes, and outputting treatment effect evaluations by conducting an RCT-represents more than routine or conventional activity. The specification describes that "MesoNet, a deep learning model that predicts the overall survival of malignant mesothelioma patients using whole slide (histology) images of tumor tissue has been developed" and that "[t]he deep learning survival predictions can outperform the existing subtype classification utilized by pathologists." As-Filed Specification, page 31, Lines 26-31. Further, "the use of MesoNet risk scores as a covariate adjustment in clinical trial primary analysis in addition to histological subtype would allow researchers to reduce the sample size requirements of three, large phase 3 clinical trials in mesothelioma." As-Filed Specification, page 31, Lines 30-33. This demonstrates that the claimed approach provides specific technical improvements over conventional methods. Dependent claims 43 and 52 further recite specific technical steps including "extracting a plurality of feature vectors of the histology image by applying a first convolutional neural network" and "classifying the histology image using at least the plurality of feature vectors and a classification model." These are not generic computer functions but specific technical implementations. Dependent claims 44 and 53 recite "computing an artificial intelligence (AI) risk score based on the histology image using a machine learning model, the machine learning model having been trained by processing a plurality of training images to predict a prognosis of the cancer patient." This represents a specific technical application, not a generic machine learning implementation.
Applicant’s arguments about MesoNet is not commensurate with the scope of the claims. Using MesoNet risk scores as a covariate adjustment to allow researchers to reduce sample size is not claimed but is also an intended result or effect of a judicial exception of a mathematical concept of adjusting sample size.
From MPEP 2106.04(a)(2) I C on mathematical calculations:
C. Mathematical Calculations
“A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
Examples of mathematical calculations recited in a claim include:
i. performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. InvestPic, LLC, 898F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018);…”
Therefore, adjusting sample size would likely involve mathematical calculations and would be abstract.
For at least these reasons, claims 40-57 are patent eligible under 35 U.S.C. § 101, and Applicant respectfully requests withdrawal of this rejection.
The rejection is respectfully maintained but modified for the claim amendments. Also, if the invention is about statistics and reducing sample size using covariates, that would be a mathematical concept.
Applicant argues 35 USC §112 Rejection, starting pg. 10 of Remarks:
Claim Rejections - 35 U.S.C. § 112
The Examiner rejected claims 40-57 under 35 U.S.C. § 112(a) as allegedly failing to comply with the written description requirement. Office Action, page 7. Specifically, the Examiner asserted that claim 40 recites "outputting an evaluation of a specific treatment effect for the cancer patient" where "there is no teaching of outputting an evaluation of a specific treatment effect for a cancer patient, only conduct a study to evaluate a treatment effect in cancer patients (plural)." Office Action, page 7. Applicant respectfully traverses this rejection.
Claims 40 and 49 have been amended to recite "outputting an evaluation of a specific treatment effect for the cancer patients by conducting a randomized controlled trial (RCT)" (emphasis added). This amendment is fully supported by the specification. The specification explicitly teaches that "the RCT is conducted to evaluate a treatment effect in cancer patients." As-Filed Specification, page 4, Lines 27-28. The specification further explains that the methods disclosed herein provide "methods for designing (e.g., reducing) sample size in a trial based on known correlation of the adjustment covariate and outcome" and "methods for reestimating and readjusting sample size while maintaining the blindness of the trial, e.g., conducting blinded sample size reestimation, based on data that becomes available during the trial." As-Filed Specification, page 3, Lines 17-22. The specification also teaches that "the present disclosure provides verification that such covariate adjustment methods using deep learning prognostic covariates can improve the statistical power achieved based on the same sample size, and can reduce sample size needed to achieve the same statistical power." As-Filed Specification, page 11, Lines 7-10. Additionally, the specification describes specific examples of evaluating treatment effects through RCTs, including semi-synthetic simulations based on clinical trial data for hepatocellular carcinoma and mesothelioma. As-Filed Specification, page 28, Lines 1-15; page 31, Lines 26-33.
The claims as amended clearly recite subject matter that was described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventors had possession of the claimed invention. Applicant therefore respectfully requests withdrawal of the rejection under 35 U.S.C. § 112(a).
There is no teaching as indicated above about a specific treatment effect for cancer patients.
The Examiner rejected claims 40-57 under 35 U.S.C. § 112(b) as allegedly being indefinite. Office Action, page 8. Specifically, the Examiner asserted that claim 40 recites "adjusting the prognostic covariate based on a number of events for a time-to-event outcome identified for the cancer patient" where "adjusting based on a number of events for a time-to event outcome for the patient is indefinite" because "[a] patient would have one event (e.g., relapse/death/etc.), not a number of events" and that it is "indefinite as to adjusting the covariate based on one patient, where the specification teaches number of events based on patients (plural) for statistical power." Office Action, page 8. Applicant respectfully traverses this rejection.
Claims 40 and 49 have been amended to recite "adjusting the prognostic covariate based on a number of events for a time-to-event outcome identified for the cancer patients" (emphasis added). This amendment clarifies that the adjustment is based on events across multiple cancer patients, consistent with the specification's teaching that the number of events is determined across a patient population for statistical power calculations. The specification explains that "[t]he induced reduction in sample size, driven by reduction in number of events to achieve a targeted statistical power, can be estimated based on a known correlation between the adjustment covariate and the outcome." As-Filed Specification, page 10, Lines 17-20. The specification further teaches that "outcome incidence" refers to the ratio of "number of events" to "sample size," where events are tracked across the trial population. As-Filed Specification, page 13, Lines 1-3.
Claims 40 and 49 have also been amended to recite "outputting an evaluation of a specific treatment effect for the cancer patients by conducting a randomized controlled trial (RCT)" (emphasis added). This amendment addresses the Examiner's concern regarding "outputting an evaluation of a specific treatment effect" by clarifying that the evaluation is for cancer patients (plural) in the context of conducting an RCT, which is consistent with the specification's teaching that "the RCT is conducted to evaluate a treatment effect in cancer patients." As-Filed Specification, page 4, Lines 27-28.
The claims as amended clearly define the subject matter with sufficient particularity that one of ordinary skill in the art would understand the scope of the claimed invention. Applicant therefore respectfully requests withdrawal of the rejection under 35 U.S.C. § 112(b).
The 35 USC 112(b) Rejection is withdrawn based on the claim amendments. However, the amendments have resulted in a new rejection.
Respectfully, it is unclear why Applicant is claiming a “specific” treatment effect when it is not taught and what exactly is the specific treatment effect based on a random trial?
Applicant argues 35 USC §103 Rejection, starting pg. 12 of Remarks:
Claim Rejections - 35 U.S.C. @ 103
Claims 40-42, 44-51, and 53-57 were rejected under 35 U.S.C. § 103 as allegedly being unpatentable over U.S. Patent Application Publication No. 2023/0360758 to Casale (hereinafter "Casale et al." or "US'758") in view of U.S. Patent Application Publication No. 2022/0359084 to Curtis (hereinafter "Curtis et al." or "US'084").
Claims 43 and 52 were rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Casale et al. in view of Curtis et al. and further in view of U.S. Patent Application Publication No. 2024/0371520 to Vincent Salomon (hereinafter "Vincent Salomon et al." or "US'520").
US'758 mentions in paragraph [0216] to identify predictive images for determining a covariate of interest. It is further mentioned to predict covariates from biopsy embeddings. However, it is not described to use a deep learning model to obtain a covariate for adjustment, wherein the deep learning model outputs "a covariate for adjustment, the deep learning model trained on histopathological slides obtained from cancer patients, and the covariate comprising a prognostic covariate for a cancer patient based on the histopathological slides," as recited by claims 40 and 49.
Applicant is reminded of piecemeal analysis…
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The secondary art of Curtis et al. was used to teach covariate adjustment and deep learning.
Importantly, Casale et al.'s abstract describes "determining... an association between each candidate covariant of a plurality of candidate covariants and the phenotype state to identify the covariant of interest." See Casale et al., Abstract. This reveals that Casale et al.'s covariates are inputs to association testing to identify genetic variants-not outputs of a deep learning model used for adjustment in clinical trial design. The purpose of Casale et al.'s system is "using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers." See Casale et al., paragraph [0002]. This is fundamentally different from the claimed invention, which uses deep learning prognostic covariates specifically to design RCTs and reduce sample size requirements. As described in the specification, the methods disclosed herein "can incorporate novel sources of prognostic signals, e.g., prognostic scores obtained by deep learning, as adjustment covariates to compute the adjusted (e.g., reduced) sample size." As-Filed Specification, page 3, lines 25-26.
Both Casale et al. and instant claims are about using machine learning with covariant analysis for clinical trials.
Furthermore, US'758 does not describe the features of "adjusting the prognostic covariate based on a number of events for a time-to-event outcome identified for the cancer patients" and "outputting an evaluation of a specific treatment effect for the cancer patients by conducting a randomized controlled trial (RCT)," as recited by claims 40 and 49. Notably, the Examiner's recitation of claim 40 in the Office Action omits the "by conducting a randomized controlled trial (RCT)" language. See Office Action, page 3. Neither Casale et al. nor Curtis et al. mentions "randomized controlled trial" or "RCT" anywhere in their respective disclosures.
Applicant has amended their claims to add RCT (randomized controlled trial).
US'084 does also not disclose using a deep learning model to output a covariate for adjustment. In paragraph [0275] of US'084, the clinical covariates are used along other features as inputs of machine learning methods in order to predict integrative subtype or binary high versus low risk of relapse labels. There is no deep learning model outputting a covariate for adjustment, as defined in the claims.
New prior art is cited that teaches the amended claim.
Furthermore, US'084 merely states "adjusted clinical covariates" without providing further details on how the clinical covariates are adjusted. See Curtis et al., paragraph [0291]. Specifically, US'084 does not describe to "adjust the prognostic covariate based on a number of events for a time-to-event outcome identified for the cancer patients," as recited by claims 40 and 49. Curtis et al.'s use of "adjusted clinical covariates" in the context of Cox Proportional Hazard models refers to using covariates as control variables in outcome analysis-this is conceptually different from adjusting a deep-learning-derived prognostic covariate based on event counts for sample size calculations in RCT design. The specification teaches that "[t]he induced reduction in sample size, driven by reduction in number of events to achieve a targeted statistical power, can be estimated based on a known correlation between the adjustment covariate and the outcome." As-Filed Specification, page 10, lines 17-20. This specific adjustment methodology is absent from Curtis et al.
Curtis teaches prediction using adjusted covariates to better compare the improvement in prediction with respect to clinical covariates for each timepoint. Therefore, various adjusted covariates were used for each timepoint (time-to-event outcome) in para. [0291] for outcome analysis. Also, claim limitations from the specification are not read into the claim.
In addition, US'084 does not mention to conduct a randomized controlled trial, wherein an evaluation of a specific treatment effect for the cancer patients is output "by conducting a randomized controlled trial (RCT)," as recited by claims 40 and 49. As noted above, neither Casale et al. nor Curtis et al. discloses or suggests conducting an RCT as part of the claimed method for outputting an evaluation of a specific treatment effect.
New prior art is cited that teaches the amended claim.
Thus, even if the skilled person were to combine US'758 with US'084, the skilled person would still not arrive at a method according to claim 40 or a non-transitory machine-readable medium according to claim 49. The combination fails to teach or suggest: (1) a deep learning model that outputs a prognostic covariate for adjustment in clinical trial design; (2) adjusting the prognostic covariate based on a number of events for a time-to-event outcome; and (3) outputting an evaluation of a specific treatment effect by conducting an RCT.
In view of the above, each of independent claims 40 and 49 are non-obvious over the prior art.
For at least these same reasons, Applicant respectfully submits that the dependent claims are non-obvious as well.
New prior art is cited that teaches the amended claim.
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 40-57 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 40-57 are directed to a method or product, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method Claim 40 as the claim that represents the claimed invention for analysis and is similar to product claim 49.
Claim 40 recites the limitations of:
A method for conducing cancer treatment effects comprising:
outputting, by a deep learning model, a covariate for adjustment, the deep learning model trained on histopathological slides obtained from cancer patients, and the covariate comprising a prognostic covariate for a cancer patient based on the histopathological slides;
adjusting the prognostic covariate based on a number of events for a time-to-event outcome identified for the cancer patients; and
outputting an evaluation of a specific treatment effect for the cancer patient by conducting a randomized controlled trial (RCT).
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim recites elements, in non-bold above, which covers performance of the limitation as managing personal behavior. Obtaining histopathological slides from cancer patients and adjusting a covariate based on a number of events for the cancer patient is following rules or instructions, and outputting evaluation of a specific treatment for the cancer patient by conducting a randomized controlled trial is teaching, therefore, managing personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as managing personal behavior, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claim 49 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
In as much as adjusting the prognostic covariate based on number of events and outputting an evaluation of a specific treatment can be done in the mind of a person, with pen and paper, the claims also recite abstract elements under Mental Processes grouping of abstract ideas.
This judicial exception is not integrated into a practical application. In particular, the claims only recite: non-transitory machine-readable medium, processing units (Claim 49). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The outputting by a deep learning model a covariate and deep learning model trained on histopathological slides is recited at a high level of generality. There is also no indication of an improvement to deep learning model technology. The claims also recite “outputting an evaluation of a specific treatment” which is not providing the patient with a particular treatment for a particular disease or condition (e.g., injecting a patient with a particular drug for treatment of a disease). Further, there is no teaching of a “specific treatment effect” in the specification for a patient and what that specific treatment is and what disease is being treated. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 40 and 49 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Steps such as outputting (transmitting) are steps that are considered insignificant extra solution activity and mere instructions to apply the exception using general computer components (see MPEP 2106.05(d), II). Thus claims 40 and 49 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 41-48 and 50-57 further define the abstract idea that is present in their respective independent claims 40 and 49 and thus correspond to Certain Methods of Organizing Human Activity and Mental Processes, and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Claims 43 and 52 recite “applying a first convolutional neural network” which is applying the model at a high level of generality. Claims 44 and 53 recite “computing an artificial intelligence (AI) risk score…using a machine learning model” which is applying a generic machine at a high level of generality. Claims 45 and 54 recite “computing a clinical risk score” and “using a clinical model, the clinical model trained using one or more subject attributes” where computing and trained are recited as a high level of generality. Therefore, the claims 41-48 and 50-57 are directed to an abstract idea. Thus, the claims 40-57 are not patent-eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 40-57 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 40 recites “outputting an evaluation of a specific treatment effect for the cancer patients by conducting a randomized controlled trial (RCT)” where there is no teaching of outputting an evaluation of a specific treatment effect for a cancer patients by conducting a randomized trial, only conduct a study to evaluate a treatment effect in cancer patients (plural).
The specification teaches:
“In some embodiments, the time-to-event outcome is overall survival, disease free survival, or time to disease relapse. In some embodiments, the RCT is conducted to evaluate a treatment effect in cancer patients. In some embodiments, the cancer is hepatocellular carcinoma, mesothelioma, pancreatic cancer, lung cancer, or breast cancer.” (pg. 3, lines 24-27)
Therefore, a trial is conducted to evaluate a treatment effect. This is not the same as evaluate a specific treatment effect for a cancer patient. For examination purposes this is interpreted as outputting an evaluation of a treatment effect for the cancer patients by conducting a RCT. Claim 40 has a similar problem.
Claims 41-48 and 50-57 are further rejected as they depend from their respective independent claim.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 40-57 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 40 recites “outputting an evaluation of a specific treatment effect for the cancer patients by conducting a randomized controlled trial (RC)” where it is indefinite as to conducting a randomized trial for a specific treatment effect. There is no teaching or definition as to exactly what the specific treatment effect is for a random trail. For examination purposes, this is interpreted as outputting a treatment effect by conducting a randomized controlled trial.
Claims 41-48 and 50-57 are further rejected as they depend from their respective independent claim.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 40-42, 44-51, and 53-57 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2023/0360758 to Casale et al. in view of Pub. No. US 2022/0359084 to Curtis et al. and in view of Pub. No. US 2022/0139505 to Ennist et al.
Regarding claims 40 and 49
(claim 40) A method for conducing cancer treatment effects comprising:
outputting, by a deep learning model, a covariate for adjustment, the deep learning model trained on histopathological slides obtained from cancer patients, and the covariate comprising a prognostic covariate for a cancer patient based on the histopathological slides;
Casale et al. teaches:
Tissue slides from biopsy (histopathological slides)…
“With reference to FIG. 2, an exemplary system (e.g., one or more electronic devices) can obtain a plurality of medical images obtained from a group of clinical subjects. The medical images depict a state of a disease of interest. In some embodiments, the plurality of medical images comprises a plurality of biopsy images of biopsy samples from the group of clinical subjects. For example, in a biopsy, one or more tissue slides can be obtained from a subject, and one or more digital images can be taken to capture each tissue slide.” [0237]
Biopsy of cancer patients…
“In an exemplary workflow depicted in FIG. 3A, biopsies 1-n have been performed. Biopsies 1-n may correspond to multiple subjects (e.g., cancer patients) and/or multiple visits (e.g., screening visit, follow-up visit). In the depicted example, the disease of interest is non-alcoholic steatohepatitis (NASH) and the biopsies are H&E stained liver biopsies from a number of clinical trials (however similar workflows may be implemented for other dieses of interest). Each biopsy (e.g., biopsy 1) results in one or more biopsy images (e.g., medical image(s) 302 for biopsy 1). Thus, biopsy 1-n result in a plurality of medical images including medical image(s) 302, medical image(s) 352, etc.” [0238]
Predictive (prognostic) images for determining covariate (prognostic covariate)…
“In some embodiments, predictive images can be identified for determining a covariate of interest. In some embodiments, predictive features may be generated at the tile-level in histopathology data. This may be accomplished using various techniques. For example, a mean aggregate tile embedding may be obtained to generate biopsy embeddings. A linear model may be fit using this data such that covariates can be predicted from biopsy embeddings. A linear model may also be applied to the tile embeddings to generate tile-level scores. As another example, multiple instances of a machine learning model may be fit directly on tile embeddings. For instance, a model may be fit that predicts both a score and a weight for each tile and then performs a weighted average of the scores. Both the score and the weight(s) may be considered as two-dimensional predictive features.” [0216]
Generator and discriminator are neural networks (deep learning model)…
“In some embodiments, the GAN model is a conditional GAN model. For instance, the generator may be configured to receive an embedding, as the condition, and a noise and output a simulated image. The discriminator may be configured to receive an input image, which may be a simulated image or a real image, and classify the input image as simulated or real. During training, the generator generates simulated images, and the simulated images and real images are provided to the discriminator for classification. Based on the outputs of the discriminator, the generator and the discriminator can be updated accordingly to minimize loss. In some embodiments, the generator and the discriminator are neural networks.” [0341]
Cancer patients…
“In an exemplary workflow depicted in FIG. 3A, biopsies 1-n have been performed. Biopsies 1-n may correspond to multiple subjects (e.g., cancer patients) and/or multiple visits (e.g., screening visit, follow-up visit). In the depicted example, the disease of interest is non-alcoholic steatohepatitis (NASH) and the biopsies are H&E stained liver biopsies from a number of clinical trials (however similar workflows may be implemented for other dieses of interest). Each biopsy (e.g., biopsy 1) results in one or more biopsy images (e.g., medical image(s) 302 for biopsy 1). Thus, biopsy 1-n result in a plurality of medical images including medical image(s) 302, medical image(s) 352, etc.” [0238]
See Covariate and Adjusting below.
adjusting the prognostic covariate based on a number of events for a time-to-event outcome identified for the cancer patients; and
Discover associations with covariates…
“The embodiments described herein are merely exemplary and the discovery platform can be applied to discover associations between any phenotype of interest and a covariate. In some examples described herein, the phenotypic data comprises medical images; the phenotype of interest is a disease of interest (e.g., NASH), which can be represented by a medical diagnosis score (e.g., fibrosis score); the covariate of interest is a genetic variant of interest. However, it should be understood that the techniques described herein can be applied to discover associations between another phenotype of interest and another covariate. Exemplary phenotypic data include, but are not limited to, which is not to suggest that other listings are limiting, in vivo medical images (e.g., MRI, X-ray, CT scan), medical images generated from biopsy samples, such as histopathology data (e.g., H&E stained, Trichrome), clinical biomarker data (e.g., blood test measurements, including proteomic and cfDNA, cognitive/psychiatric assessment scores, microbiome assessment, etc.) and genomic biomarker data (e.g., bulk RNA-seq, methylation data, genomic sequence data, epigenetic sequence data, etc.). Exemplary phenotypes include, but are not limited to, which is not to suggest that other listings are limiting, a disease of interest, gene expression, metabolomics, proteomics, transcriptomics, or lipidomics, etc. Exemplary covariates include, but are not limited to, which is not to suggest that other listings are limiting, demographic information (e.g., age, sex), clinical covariates (e.g., a disease state, a clinical score or a blood biomarkers), genomic data (e.g., genetic data, expression data, methylation data, etc.), etc.” [0205]
Identify covariate with respect to drug response phenotype (DRP)…
“In block 140, the system may be further configured to identify a covariant of interest with respect to the DRP associated with a treatment. The imputation of DRP can be performed using clinical trial datasets as long as the progression embeddings are available. Significant associations between DRP and molecular data (e.g., expression and genetics) can be retrieved through an association test. Association with expression identifies genes that could not be detected in a placebo-vs-drug differential expression analysis. In some cases, the analysis of the DRP can identify a correlated set of genes as case control of the true placebo-vs-drug differential expression analysis. In some cases, the analysis of DRP can identify a larger set of genes due to the analysis of a larger cohort, which can help interpret correlates of the DRP.” [0227]
See Covariate and Adjusting below.
outputting an evaluation of a specific treatment effect for the cancer patient by conducting a randomized controlled trial (RCT).
Example of define (outputting) treatment effects…
“The correlation metric for each genetic variant can be compared against a predefined threshold to determine whether there is an association between the genetic variant and the embeddings. In one exemplary implementation, the system uses a Bonferroni-adjusted P value threshold of 0.05 to define significant treatment histological effects and assess whether the treatment has an effect on progression embeddings. In some embodiments, only treatments with significant P values are further studied, for example, using the process 2000 in FIG. 20.” [0381]
Another example of assess (outputting) treatment (singular therefore specific) efficacy, where it is restricted to patients with a cluster (therefore applicable to a patient within the cluster)…
“The system can also assess treatment efficacy within different clusters of patients, for example by using the linear model testing procedure described with respect to model 316 in FIG. 3B to test for association between treatment and the clinical endpoint considering only patients in a given cluster. For example, the system can fit a cluster-specific model that receives a binary indicator for treatment vs placebo and outputs a clinical endpoint (e.g. fibrosis progression). The analysis may then be restricted to patients within a specific cluster.” [0450]
See Adjusting Covariate and Random Control below.
Covariate and Adjusting
Casale et al. teaches covariate and cancer. They do not teach adjust covariate.
Curtis et al. also in the business of covariate and cancer teaches:
Covariate and time to event…
“Cox proportion hazard models are statistical survival models that relate the time that passes to an event and the covariates associated with that quantity in time (See D. R. Cox, J. R. Stat. Soc. B 34, 187-220 (1972), the disclosure of which is herein incorporated by reference). To utilize Cox proportional hazards models, in some embodiments, clinical, molecular, and integrative subtype features are included. In some embodiments, features can be linear and/or polynomial transformed and interaction can include variable selection. In some embodiments, to further simplify the model, stepwise variable selection can be incorporated into the cross validation scheme. Any appropriate computational package can be utilized and/or adapted, such as (for example), the RMS package (https://www.rdocumentation.org/packages/rms).” [0170]
Covariate with neural network (deep learning)…
“To perform the analysis, genomic copy number from a SNP6 array consisting of 1,191,855 segments spanning the entire genome was utilized. Each segment denoted the average copy number in that region. In order to both reduce the dimensionality and obtain useful features, the CNRegions function from the iClusterPlus R package were used to merge adjacent regions and obtain a final set of 4794 consistent copy number regions for each sample (of the 1285 patients in the dataset), with adjusted mean copy number values for each region. These were used as features, alongside the clinical covariates such as age at diagnosis, tumor grade, tumor size, and number of tumor-positive lymph nodes in machine learning methods to predict integrative subtype or binary high [IC 1, 2, 6, 9] versus low [IC 3, 4, 7, 8] risk of relapse labels. The performance of various models including logistic regression, support vector machines with a linear kernel, support vector machines with a gaussian kernel, and neural networks were evaluated to determine their ability to accurately predict integrative subtype risk labels from genome-wide copy number data (FIG. 30). While multiple models performed well, the neural network has the strongest performance among the different models, with both the highest AUROC and the highest AUPRC.” [0275]
Outcome associations and time to relapse or death (time to event)…
“The METABRIC dataset was used to generate signatures from gene expression data as detailed in Curtis, et al., (2012), cited supra. Outcome associations, including late relapse, of the METABRIC cohort were also calculated as detailed in Example 1. In this example, the data was limited to ER+/HER2− samples (n=1398). Late relapse is defined as relapse that occurs after 5 years without any previous incidents of relapse after surgery (i.e., relapse free at year 5). Two outcomes were considered, distant relapse free survival and relapse free survival. Distant relapse free survival is defined as time to distance relapse. Relapse free survival is defined as time to distant relapse or disease specific death.” [0290]
Outcome analysis and adjusted clinical covariates based on timepoints…
“To perform outcome analyses, Kaplan Meier plots were generated using the survival packages (model using survfit function) and survminer (plt, using ggsurvplot function. P-values were generated using Logrank test. Hazard ratio was calculated with hazard.ratio function from survcomp package, which was used to measure the effect size of the signature. Concordance Index (C-Index) was calculated using concordance.index from survcomp package. Area under the curve was used to evaluate the prediction performance of the signatures in different time points. Uno's AUROC from AUC.uno function of survAUC package was used to calculate AUROC. To better compare the improvement in prediction with respect to clinical covariates, for each timepoint, the AUC was calculated using a Cox Proportional Hazard model using the risk or the scores along with adjusted clinical covariates. A 20×10-fold cross validation was performed to avoid overfitting in the overestimation of the AUC.” [0291]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Casale et al. the ability to adjust covariates as taught by Curtis et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Curtis et al. who teaches the advantages of improvement in prediction using adjusted covariates.
Adjusting Covariate and Random Control
The combined references teach adjustment of covariance. They do not teach with a randomized controlled trial.
Ennist et al. also in the business of adjusting covariance teaches:
Predictions for time-to-event…
“Step 102 of method 100 includes building one or more predictive models for predicting progression of the condition in patients. A predictive model is built to generate a prediction for future condition progression for a patient based on clinical data for the patient. Predictions can include various metrics typically used as outcomes in clinical trials. Examples of types of prediction models are regression models and time-to-event models. A regression model can predict patient ability or condition state at a point in time in the future, while a time-to-event model determines the log likelihood that a patient will reach a certain ability (disability) or condition state, including death.” [0065]
Deep learning…
“Any of numerous predictive modeling techniques known to those of skill in the art can be used to create the predictive model, including regression and machine learning techniques. Regression techniques could include, but are not limited to, linear regression, logistic regression, Cox proportional hazards model, time series models, classification and regression trees and multivariate adaptive regression splines. Machine learning techniques could include k-nearest neighbors, deep learning algorithms, convolutional neural networks, support vector machines, or naïve Bayes. These modeling techniques can be used to perform variable reduction in order to select the most predictive feature set followed by building a working predictive model. Models can be combined using known methods to produce ensemble models with possible increased accuracy.” [0068]
Example of trial with control group (controlled trial)…
“The effectiveness of the trial on the patients in a subgroup is evaluated by analyzing the data in the associated subset of clinical trial patient data. For example, the clinical trial outcomes measured during the clinical trial are compared between the treatment group and the control group to generate a measure of efficacy of the treatment. The measure of efficacy for any given subgroup may be less than, equal to, or greater than a measure of efficacy for the full analysis set—the full set of data for which the success of the previously conducted clinical trial was judged. The measure of efficacy may be, for example, a probability value (p-value) or any other suitable value for measuring the efficacy of a treatment in a clinical trial, or more generally, the outcome of a study.” [0082]
Principles from above (control trial) extended to application below…
“The principles, methods, and systems described above, can be extended to a number of different applications, as will be readily recognized by one of ordinary skill in the art. Examples of application that are within the scope of the present disclosure are described further below.” [0126]
Prediction based covariate adjustment for a trial, to adjust covariate analysis for measured outcome and the adjustment will boost the study power to detect (output) a treatment effect…
“Prediction based Covariate Adjustment—In this application, predictions are made at the beginning of a trial, using patient data measured before and up to the moment that the patient begins treatment (the baseline visit). Those predictions are additional baseline data (“covariates”) that can be used to adjust the analysis using traditional statistical means. The more predictive a baseline covariate is of the measured outcome of the trial (the trial “endpoint” that is measured), the more the adjustment will boost the study power (the likelihood that the study will be able to detect a real treatment effect if it is there.” [0127]
Example of randomizing a trial…
“Prediction based Randomization—In this application, predictions are used to define multiple strata (e.g. 3 groups of predicted “fast” “average” and “slow” progressing patients). Each of these three groups has separate randomization schedules, to assign patients to various arms of the study (e.g. placebo, high dose, middle dose, low dose). By randomizing in this way, all arms of the trial should have similar proportions of “fast” “average” and “slow” patients. Additionally, because the prediction algorithm leverages dozens of input variables, the likelihood of having a confounding variable (a statistically-significant difference in that variable between two study arms) can be reduced across all of those input variables.” [0128]
“One or more of the above application can be combined. For example, the virtual controls application can augment a concurrent placebo control. The predictions of the virtual control are combined with to the observation data from the placebo-arm patients. This creates a larger placebo arm for comparison to the observed treatment data. Another combination may be the combination of randomization application with the covariate adjustment application. According to some embodiments, the trial enrichment application is combined with the covariate adjustment application, and in other embodiments, the trial enrichment, randomization, and covariate adjustment applications are combined.” [0131]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references to conduct a randomized control trial with covariate adjustment as taught by Ennist et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Ennist et al. who teaches the advantages of covariate adjustment to boost the study power of a trial outcome.
Regarding claims 41 and 50
(claim 41) The method of claim 40, wherein the time-to-event outcome is overall survival, disease free survival, or time to disease relapse.
The combined references teach time-to-event and survival.
Curtis et al. also in the business of covariate and cancer teaches:
Covariate and time to event…
“Cox proportion hazard models are statistical survival models that relate the time that passes to an event and the covariates associated with that quantity in time (See D. R. Cox, J. R. Stat. Soc. B 34, 187-220 (1972), the disclosure of which is herein incorporated by reference). To utilize Cox proportional hazards models, in some embodiments, clinical, molecular, and integrative subtype features are included. In some embodiments, features can be linear and/or polynomial transformed and interaction can include variable selection. In some embodiments, to further simplify the model, stepwise variable selection can be incorporated into the cross validation scheme. Any appropriate computational package can be utilized and/or adapted, such as (for example), the RMS package (https://www.rdocumentation.org/packages/rms).” [0170]
Time with relapse…
“Breast cancer has multiple stages of progression (i.e., a multistate disease), with clinically relevant intermediate endpoints such as recurrence in loco-regional or distant locations. These recurrence events are correlated, and individual survival analyses of one endpoint cannot fully capture patterns of recurrence that may be associated with differential prognosis. A patient's prognosis can differ dramatically depending on when and where a relapse occurs, time since surgery, and time since loco-regional or distant relapse. These distinct states and timescales are generally not accounted for and motivate the development of a unified statistical framework, as proposed here.” [0249]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use time-to-event such as survival or relapse as taught by Curtis et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Curtis et al. who teaches the use of events for evaluating covariates.
Regarding claims 42 and 51
(claim 42) The method of claim 40, wherein the cancer is hepatocellular carcinoma, mesothelioma, pancreatic cancer, lung cancer, or breast cancer.
Casale et al. teaches:
Cancer patients…
“In an exemplary workflow depicted in FIG. 3A, biopsies 1-n have been performed. Biopsies 1-n may correspond to multiple subjects (e.g., cancer patients) and/or multiple visits (e.g., screening visit, follow-up visit). In the depicted example, the disease of interest is non-alcoholic steatohepatitis (NASH) and the biopsies are H&E stained liver biopsies from a number of clinical trials (however similar workflows may be implemented for other dieses of interest). Each biopsy (e.g., biopsy 1) results in one or more biopsy images (e.g., medical image(s) 302 for biopsy 1). Thus, biopsy 1-n result in a plurality of medical images including medical image(s) 302, medical image(s) 352, etc.” [0238]
The combined references teach cancer. They do not teach breast cancer.
Curtis et al. also in the business of cancer teaches:
Breast cancer…
“Various embodiments are directed towards methods treatments for breast cancer based on its molecular characterization. In various embodiments, the molecular subtype of a breast cancer is determined based on its genetics. In various embodiments, a molecular subtype is indicative breast cancer aggressiveness and risk of relapse. In various embodiments, a molecular subtype is indicative of the molecular pathology of a breast cancer. In various embodiments, a breast cancer is treated based upon aggressiveness, risk of relapse, and molecular drivers as determined by its molecular subtype.” [0004]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to consider various cancers such as breast cancer as taught by Curtis et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by the combined references that teach cancer, it would be obvious to consider various types.
Regarding claims 44 and 53
(claim 44) The method of claim 40 further comprising:
obtaining a digital histology image of a histology section from the cancer patient;
Casale et al. teaches:
Digital images captured (obtaining) using biopsy of tissue slides (histology section)…
“With reference to FIG. 2, an exemplary system (e.g., one or more electronic devices) can obtain a plurality of medical images obtained from a group of clinical subjects. The medical images depict a state of a disease of interest. In some embodiments, the plurality of medical images comprises a plurality of biopsy images of biopsy samples from the group of clinical subjects. For example, in a biopsy, one or more tissue slides can be obtained from a subject, and one or more digital images can be taken to capture each tissue slide.” [0237]
dividing the digital image into a set of tiles;
Image split (dividing) into image tiles…
“With reference to FIG. 3A, a medical image can be split into a plurality of image tiles. For example, medical image(s) 302 of biopsy 1 can be split to obtain image tiles 306-1, 306-2, . . . , 306-M.sub.1; medical image(s) 352 of biopsy n can be split to obtain image tiles 356-1, 356-2, . . . , 356-M.sub.n. In some embodiments, image tiles can be extracted from a medical image using a predefined grid and stored as image tiles of a uniform size. In one exemplary implementation, the image tiles are extracted using a predefined grid with tile dimensions 192 m×192 m and the image tiles are saved as images sized 224 pixels×224 pixels.” [0243]
extracting a plurality of feature vectors from the set of tiles, or a subset thereof; and
Translating (extracting) input image into embedding, where embedding can be a vector representation (extracting vector) of input image tile…
“FIG. 4A illustrates an exemplary unsupervised machine-learning model used in block 202. With reference to FIG. 4A, an unsupervised machine-learning model 404 may be configured to receive an input image tile 402 (e.g., one of the image tiles in FIG. 3A) and provide an output tile embedding 406. Tile embedding 406 can be a vector representation of an input image tile 402 (e.g., tile 306-1) in the latent space. Translating an input image into an embedding can significantly reduce the size and dimension of the original data. As an example, an image tile sized 224 pixels×224 pixels can be reduced to a 2,048-dimensional vector. The lower-dimension embedding can be used for downstream processing 476, as described below.” [0246]
computing an artificial intelligence (Al) risk score based on the histology image using a machine learning model, the machine learning model having been trained by processing a plurality of training images to predict a prognosis of the cancer patient,
Image and risk score of likelihood (predict) subject incurring disease of interest…
“In some embodiments, the data associated with a medical image can include genetic data of the subject from whom the biopsy sample is taken. For example, the data (e.g., associated data 304) can include the subject's genetic information related to a plurality of genetic variants (e.g., 100,000 variants, 1 million variants, 10 million variants). For example, the data can indicate whether the subject has each of the plurality of genetic variants. For example, for a genetic variant with two alleles in the population, the medical image may be associated with a genetic variant value of 0, 1, or 2 depending on if the individual has 0, 1, or 2 copies of the least frequent allele. In some embodiments, the genetic data of the subject may be a polygenic risk score, indicating a likelihood of the subject incurring a disease of interest.” [0241]
Example of using unsupervised machine learning (AI) model for score…
“An exemplary method of identifying at least one genetic variant of interest with respect to a disease of interest comprises: inputting a plurality of medical images obtained from a group of clinical subjects into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space, each embedding corresponding to a phenotypic state relative to the disease of interest reflected in one or more of the plurality of medical images; inputting each embedding of the plurality of embeddings into a trained linear regression model to receive a predicted continuous medical diagnosis score for each embedding of the plurality of embeddings to obtain a plurality of predicted medical diagnosis scores, each predicted continuous medical diagnosis score indicative of a state of the disease of interest; associating the plurality of predicted continuous medical diagnosis scores with each candidate genetic variant of a plurality of candidate genetic variants expressed by the group of clinical subjects from whom the plurality of medical images was taken; determining, based on the association, a correlation metric between the disease of interest and each candidate genetic variant to identify the at least one genetic variant of interest from the plurality of candidate genetic variants, the correlation metric indicative of the impact of the candidate genetic variant on the disease of interest.” [0020]
Example of artificial neural network (AI)….
“At block 1610, the system may generate a classification model for determining whether a patient has received the placebo or the treatment based on the plurality of treatment progression embeddings, wherein outputs of the classification model are indicative of a drug response histological phenotype (DRP). With reference to FIG. 17, the system generates a DRP classification model 1730 based on placebo progression embeddings 1722 and treatment progression embeddings 1726. In some embodiments, the classification model (e.g., DRP classification model 1730) is configured to receive an input progression embedding and output a classification result indicating whether a patient has received the placebo or the treatment. The classification model can be implemented, for example, as a logistic regression model, an artificial neural network model, a random forest model, a naïve Bayes model, etc.” [0367]
wherein the Al risk score quantifies the prognosis of the cancer patient.
Example of cancer patients with NASH…
“In an exemplary workflow depicted in FIG. 3A, biopsies 1-n have been performed. Biopsies 1-n may correspond to multiple subjects (e.g., cancer patients) and/or multiple visits (e.g., screening visit, follow-up visit). In the depicted example, the disease of interest is non-alcoholic steatohepatitis (NASH) and the biopsies are H&E stained liver biopsies from a number of clinical trials (however similar workflows may be implemented for other dieses of interest). Each biopsy (e.g., biopsy 1) results in one or more biopsy images (e.g., medical image(s) 302 for biopsy 1). Thus, biopsy 1-n result in a plurality of medical images including medical image(s) 302, medical image(s) 352, etc.” [0238]
Example of predicted (prognosis) score…
“In some embodiments, the plurality of predicted medical diagnosis scores comprises a disease progression score obtained as the difference of predicted medical diagnosis scores at different measurements during the clinical trial.” [0043]
“In block 150, the system may further be configured to evaluate a treatment with respect to progression of a disease of interest. The disease progression can be quantified by continuous medical diagnosis scores. Significant associations between high resolution NASH scores and various treatments are retrieved through an association test. This process can retrieve drug effects on medical diagnosis scores that could not be detected using pathologist-assigned, discrete scores. The continuous scores enable a more precise definition of disease progression, empowering longitudinal expression analysis (e.g., FIG. 26A) and genetic association studies (e.g., FIG. 26B). Details of block 150 are provided herein with reference to FIG. 22.” [0229]
Regarding claims 45 and 54
(claim 45) The method of claim 44, further comprising:
obtaining clinical attributes derived from the cancer patient;
Casale et al. teaches:
Histologically assessed (obtaining) by pathologist (clinical attribute) fibrosis…
“A predicted continuous score has strong advantages over a discrete score assigned by a pathologist. Specifically, the predicted score has a continuous value and thus captures more nuance than a pathologist-assigned, discrete score. The ability to assign continuous scores to the embeddings (and image data) results in higher precision and improved statistical power in downstream analyses, such as obtaining a closer association of each depicted disease state with a genetic variant or variants. For example, the severity of NASH and liver fibrosis is currently histologically assessed by pathologists through the NASH CRN and Ishak stage ordinal scores, such as the Ishak fibrosis score (0-6), the steatosis score (0-3), the lobular inflammation score (0-3), and the ballooning score (0-2). Quantitative analyses of these metrics are challenged by their low-resolution categorization of disease. The linear model can be trained to generate continuous scores from imaging data (e.g., H&E liver biopsy imaging data) that are predictive of pathologist scores. The continuous scores enable a more precise definition of disease progression, empowering longitudinal expression analysis and genetic association studies.” [0198]
computing a clinical risk score using a clinical model, the clinical model trained using one or more subject attributes; and
Generate (computing) using trained linear model and scores…
“A predicted continuous score has strong advantages over a discrete score assigned by a pathologist. Specifically, the predicted score has a continuous value and thus captures more nuance than a pathologist-assigned, discrete score. The ability to assign continuous scores to the embeddings (and image data) results in higher precision and improved statistical power in downstream analyses, such as obtaining a closer association of each depicted disease state with a genetic variant or variants. For example, the severity of NASH and liver fibrosis is currently histologically assessed by pathologists through the NASH CRN and Ishak stage ordinal scores, such as the Ishak fibrosis score (0-6), the steatosis score (0-3), the lobular inflammation score (0-3), and the ballooning score (0-2). Quantitative analyses of these metrics are challenged by their low-resolution categorization of disease. The linear model can be trained to generate continuous scores from imaging data (e.g., H&E liver biopsy imaging data) that are predictive of pathologist scores. The continuous scores enable a more precise definition of disease progression, empowering longitudinal expression analysis and genetic association studies.” [0198]
computing a final risk score for the subject from the Al risk score and the clinical risk score,
Risk score and likelihood of disease…
“In some embodiments, the data associated with a medical image can include genetic data of the subject from whom the biopsy sample is taken. For example, the data can include the subject's genetic information related to a plurality of genetic variants (e.g., 100,000 variants, 1 million variants, 10 million variants). For example, the data can indicate whether the subject has each of the plurality of genetic variants. For example, for a genetic variant with two alleles in the population, the medical image may be associated with a genetic variant value of 0, 1, or 2 depending on if the individual has 0, 1, or 2 copies of the least frequent allele. In some embodiments, the genetic data of the subject may be a polygenic risk score, indicating a likelihood of the subject incurring a disease of interest.” [0298]
Non-linear to predict score from image data using neural network (artificial intelligence)….
“In contrast, a supervised model (e.g., Yr1) refers to a non-linear machine-learning model (e.g., a neural network) configured to receive imaging data and predict a medical diagnosis score. Linear regression models, such as the first four models, are more computationally efficient to train and to apply than the supervised model. As shown in FIG. 14, linear models generated based on embeddings can provide a similar, and in some cases superior, predictive power than supervised machine-learning model, while requiring significantly less resources and time to train and to apply.” [0352] Inherent with similar predictive power than supervised model is that the AI models have been and can be used.
Obtain scores over time…
“In some embodiments, determining the plurality of placebo progression scores and the plurality of treatment progression scores comprises: determining, for each subject in the placebo group, a slope of a linear model fitted at least based on a baseline placebo score and a follow-up placebo score of the subject in the placebo group; and determining, for each subject in the treatment group, a slope of a linear model fitted at least based on a baseline placebo score and a follow-up placebo score of the subject in the treatment group. For example, for a patient, the system can obtain the patient's medical diagnosis scores over time (including the baseline score and the follow-up score) and fit a linear model configured to receive a dosage (or time of treatment) and predict a medical diagnosis score. The progression score for the patient can be the slope of the linear model.” [0417]
Outputs a score or end point (final score)…
“If a specific cluster is associated with progression, the system can then identify genetic and phenotypic biomarkers of that cluster (for example by using the linear model testing procedure described with respect to model 316 in FIG. 3B to test for association between cluster identity and genetics, expression, lab values, etc.). For example, the system fits a model that receives a cluster binary indicator (e.g., patient in cluster coded as 1, not in cluster coded as 0) and outputs a clinical score or end point (e.g., disease progression). The clinical end point can be quantified as a progression score or the clinical endpoint monitored in the clinical trial (e.g., whether patients have a higher or lower fibrosis score based on pathologist assessment).” [0452]
wherein the final risk score quantifies the prognosis of the cancer patient.
“FIG. 14 illustrates the performance of the various linear models configured to predict medical diagnosis scores, in accordance with some embodiments. As shown, five models are trained to: predict fibrosis scores; predict steatosis scores; predict lobular inflammation scores; and predict hepatocyte ballooning scores. For each score type, the colors of the bars are in the same order as the colors in the legend.” [0350]
Predict diagnosis score (quantifies prognosis)…
“In contrast, a supervised model (e.g., Yr1) refers to a non-linear machine-learning model (e.g., a neural network) configured to receive imaging data and predict a medical diagnosis score. Linear regression models, such as the first four models, are more computationally efficient to train and to apply than the supervised model. As shown in FIG. 14, linear models generated based on embeddings can provide a similar, and in some cases superior, predictive power than supervised machine-learning model, while requiring significantly less resources and time to train and to apply.” [0352]
Regarding claims 46 and 55
(claim 46) The method of claim 44, wherein the digital histology image is a whole slide image (WSI).
Casale et al. teaches:
Image from each slide (therefore whole slide image)…
“With reference to FIG. 2, an exemplary system (e.g., one or more electronic devices) can obtain a plurality of medical images obtained from a group of clinical subjects. The medical images depict a state of a disease of interest. In some embodiments, the plurality of medical images comprises a plurality of biopsy images of biopsy samples from the group of clinical subjects. For example, in a biopsy, one or more tissue slides can be obtained from a subject, and one or more digital images can be taken to capture each tissue slide.” [0237]
Regarding claims 47 and 56
(claim 47) The method of claim 44, wherein the histology section has been stained with a dye.
Casale et al. teaches:
Disease of interest is stained using Hematoxylin & eosin (dye)…
“In some embodiments, the discovery platform comprises a plurality of stages. At the first stage, an exemplary system (e.g., one or more electronic devices) generates embeddings based on medical imaging data related to a phenotype of interest such as a disease of interest. An embedding is a mapping of a variable to a vector (an array of numbers). As described herein, an embedding refers to a vector representation of a phenotypic state relative to the disease of interest reflected in the medical imaging data. The embedding captures rich semantic information of the medical imaging data (e.g., features of the microscopic structure of tissues reflected in the image), while excluding information that is not relevant to downstream analyses (e.g., orientation of the image). In an exemplary implementation, the disease of interest is non-alcoholic steatohepatitis (NASH) and the medical images are from hematoxylin & eosin (H&E) stained liver biopsies from a number of clinical trials. The resulting unsupervised embeddings can enable target identification, cross-clinical trial analysis, and enhance interpretability, as described herein.” [0192]
Regarding claims 48 and 57
(claim 48) The method of claim 47, wherein the dye is hematoxylin and eosin (H&E).
Casale et al. teaches:
Disease of interest is stained using Hematoxylin & eosin (dye)…
“In some embodiments, the discovery platform comprises a plurality of stages. At the first stage, an exemplary system (e.g., one or more electronic devices) generates embeddings based on medical imaging data related to a phenotype of interest such as a disease of interest. An embedding is a mapping of a variable to a vector (an array of numbers). As described herein, an embedding refers to a vector representation of a phenotypic state relative to the disease of interest reflected in the medical imaging data. The embedding captures rich semantic information of the medical imaging data (e.g., features of the microscopic structure of tissues reflected in the image), while excluding information that is not relevant to downstream analyses (e.g., orientation of the image). In an exemplary implementation, the disease of interest is non-alcoholic steatohepatitis (NASH) and the medical images are from hematoxylin & eosin (H&E) stained liver biopsies from a number of clinical trials. The resulting unsupervised embeddings can enable target identification, cross-clinical trial analysis, and enhance interpretability, as described herein.” [0192]
Claims 43 and 52 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (10) above in further view of Pub. No. US 2024/0371520 to Vincent Salomon et al.
Regarding claims 43 and 52
(claim 43) The method of claim 40 further comprising:
accessing a digital histology image of a histology section obtained from the cancer patient;
Casale et al. teaches:
Image taken (accessing) histology…
“In some embodiments, the system performs association testing between a candidate genetic variant and the disease of interest. Each medical image (e.g., histology image) has associated gene sequences from the human subject from whom the image was taken. The association testing comprises generating (e.g., fitting) a linear model based on the candidate genetic variant and the continuous scores indicative of the disease of interest. The system can generate variant-specific models (e.g., 100,000 models, 1 million models, 10 million models, etc.) for all candidate genetic variants of interest (e.g., 100,000 variants, 1 million variants, 10 million variants, etc.). Each model can be evaluated to determine if there is a significant association between each candidate genetic variant and the disease of interest to identify one or more genetic variants of interest.” [0199]
Digital image…
“With reference to FIG. 2, an exemplary system (e.g., one or more electronic devices) can obtain a plurality of medical images obtained from a group of clinical subjects. The medical images depict a state of a disease of interest. In some embodiments, the plurality of medical images comprises a plurality of biopsy images of biopsy samples from the group of clinical subjects. For example, in a biopsy, one or more tissue slides can be obtained from a subject, and one or more digital images can be taken to capture each tissue slide.” [0237]
Cancer patients…
“In an exemplary workflow depicted in FIG. 8A, biopsies 1-n have been performed. Biopsies 1-n may correspond to multiple subjects (e.g., cancer patients) and/or multiple visits (e.g., screening visit, follow-up visit) of a same subject. In the depicted example, the disease of interest is non-alcoholic steatohepatitis (NASH) and the biopsies are H&E stained liver biopsies from a number of clinical trials. Each biopsy (e.g., biopsy 1) results in one or more biopsy images (e.g., medical image(s) 802 for biopsy 1). Thus, biopsy 1-n result in a plurality of medical images including medical image(s) 802, medical image(s) 852, etc.” [0295]
extracting a plurality of feature vectors of the histology image by applying a first convolutional neural network, wherein each of the features of the plurality of feature vectors represents local descriptors of the histology image;
Obtain (extracting) vector representations of image…
“Data architecture 450 may include an encoding stage 460 within the model training 450 where each augmented image (e.g., augmented images 458a, 458b) may be encoded by a respective one of encoders 462a, 462b. Each of augmented images 458a, 458b may be passed through an encoder to obtain respective vector representations 464a, 464b in a latent space. In some embodiments, encoders 462a and 462b have shared weights. In some embodiments, each encoder 462a, 462b is implemented as a neural network. For example, an encoder can be implemented using a variant of the residual neural network (“ResNet”) architecture. As shown, encoders 462a and 462b output vector representation 464a (e.g., hi vector output by encoder 462a based on augmented image 458a) and vector representation 464b (e.g., h.sub.j vector output by encoder 462b based on augmented image 458b), respectively.” [0249]
See Vector and Convolutional Neural Network below.
classifying the histology image using at least the plurality of feature vectors and a classification model, wherein the classification model is trained using a training set of known histology images and known prognosis information; and
Classify real and fake mages using vectors and discriminator (classification model)…
“In one exemplary implementation, embedding 1102 is a 2048-dimensional embedding and noise vector 1108 is a 512-dimensional vector sampled from a standard normal distribution. Embedding 1102 and noise vector 1108 may be combined into a 512-dimensional vector t that becomes the input of the pGAN generator. The discriminator may be configured to receive an embedding x (as the condition), such as embedding 1102, as input in addition to an image i. This addition enables the discriminator to classify real and fake images also based on the consistency with the input embedding. In an exemplary implementation, the embedding is a 2048-dimensional embedding and the image is a 256×256 image.” [0343]
See Vector and Convolutional Neural Network below.
determining a likelihood of a prognosis of the cancer patient based on at least the classification of the histology image.
See Vector and Convolutional Neural Network below.
Vector and Convolutional Neural Network
The combined references teach image. They do not teach convolutional neural network and vector.
Vincent Salomon et al. also in the business of image teaches:
convolutional neural network to classify and image includes obtain (extract) vectors for each tile (image) and training…
“In an embodiment of the invention, it is provided a computer-implemented method for classifying an image comprising the following steps: [0044] dividing the image into sub-images, called tiles, [0045] encoding each tile or each selected tile, via a pre-trained model, for example via a pre-trained convolutional neural network, to obtain a representation vector or tensor for each tile concerned [0046] assigning a score, also called attention score, to each tile, [0047] generate a global representation vector or tensor by aggregating all the vectors or tensors of each concerned tile, taking into account the aforementioned scores, for instance through a weighted sum of said vectors or tensors of the tiles, where the weight is the corresponding score of the vector or tensor of said tile, [0048] classifying the image or at least a part of the image, from the global representation vector or tensor, using a decision model, for example using a pre-trained neural network, for example of the fully connected type.” [0043]
Determining prognosis based on classification and likelihood of survival (prognosis)…
“The present invention also concerns a method for determining the prognosis of a patient suffering from a cancer comprising the steps of: a1. classifying or stratifying the patient as having an HRD or a non HRD (or HRP) cancer according to the method of the invention or, a2.1. identifying a phenotypical feature, or a combination of phenotypical features or phenotypical pattern in a biological image from a subject, and a2.2. classifying or stratifying the patient based on the phenotypical feature, or combination of phenotypical features or phenotypical pattern identified in the biological image of said patient as having an HRD or HRP cancer, or a3. Classifying or stratifying the cancer tissue section r image therefore of a patient as HRD or non HRD according to the method of the invention and stratifying the patient based on the classification of said cancer tissue section or image thereof b1. determining, based at least in part on the classification of the patient as having an HRD cancer, that the patient has a relatively good prognosis, or [0186] b2. determining, based at least in part on the classification of the patient as having a non HRD cancer, that the patient has a relatively poor prognosis, optionally wherein the patient prognosis includes the patient's likelihood of survival (e.g., progression-free survival, overall survival), wherein a relatively good prognosis would include an increased likelihood of survival as compared to some reference population (e.g., average patient with this patient's cancer type/subtype, average patient not having an HRD signature, etc.). Conversely, a relatively poor prognosis in terms of survival would include a decreased likelihood of survival as compared to some reference population (e.g., average patient with this patient's cancer type/subtype, average patient having an HRD signature, etc.).” [0181] – [0187]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use convolutional neural networks and vectors as taught by Vincent Salomon et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Vincent Salomon who teaches the advantages of using convolutional neural network and vectors for classifying images and the combined references benefit as they also are directed to studying images.
Conclusion
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.
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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684
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