DETAILED ACTION
Applicant’s response, filed 01 December 2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01 December 2025 has been entered.
Claim Status
Claims 1-17 and 19-31 are pending and examined herein.
Claims 1-17 and 19-31 are rejected.
Priority
Claims 1-17 and 19-31 are granted the claim to the benefit of priority to U.S. Provisional applications 63/067748 and 63/203804 filed 19 August 2020 and 30 July 2021 respectively. Thus, the effective filling date of claims 1-17 and 19-31 is 19 August 2020.
Claim Rejections - 35 USC § 112
112/a New Matter
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 1-6, 8-11, 14-17, 19-30 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 1 recites “a predicted classification of the new subject’s event occurrence”. The MPEP states at 2163.05(I)(B) “The written description requirement for a claimed genus may be satisfied through sufficient description of a representative number of species. A "representative number of species" means that the species which are adequately described are representative of the entire genus. Thus, when there is substantial variation within the genus, one must describe a sufficient variety of species to reflect the variation within the genus”. There is an inadequate written description of a representative number of species for the genus of “event occurrences”. The instant disclosure provides a discussion of predicting risk of an event such as cancer recurrence using survival curves (instant disclosure [0246]-[0249]). However, there is not an adequate written description for a representative number of species of the genus of event occurrences which encompasses a broad range of possible events that may be classified using RNA expression data. Thus, this limitation constitutes as new matter. Dependent claims 2-6, 9-11, 14-17, 19-30 are rejected by virtue of their dependency on a rejected claim without alleviating the rejection. It is noted that claims 7, 8, 12, 13, and 31 are not rejected because they limit the event occurrence to cancer recurrence or response to treatment.
Claim 8 recites “wherein the classification of the new subject’s response to treatment comprises predicting a high-risk of a cancer type”. There is an inadequate written description for this limitation. The instant disclosure provides “an artificial intelligence engine which predicts a patient’s outcome to treatments” (instant disclosure [0232]), “the first entity may then apply their adapted dataset to the trained engine to predict patient outcomes” (instant disclosure [0232]), “survival curves provide estimates on the duration of time until an event of interest occurs for a patient, which is cancer recurrence in this experimental setup” (instant disclosure [0249]). However, there is no disclosure that the classification of the new subject’s response to treatment comprises a prediction of a high-risk of a cancer type. Thus, this limitation constitutes as new matter.
112/b
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 1-17 and 19-31 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 1 recites “wherein the second dataset is inaccessible by the first entity” which renders the metes and bounds of the claim indefinite. The MPEP provides that “a "wherein" clause limited a process claim where the clause gave "meaning and purpose to the manipulative steps” (MPEP 2111.04(I)) and the indefiniteness arises because it is unclear if this limitation is meant to limit a manipulative step of the method or if this limitation is meant to be an intended use of the method. If this limitation is meant to further limit the transforming step it is unclear what step in the transforming step this limitation is meant to further limit due to the transforming steps using the second data set. It is further unclear if the second dataset is inaccessible by the first entity itself or if the second dataset is also inaccessible to the computer system associated with the first entity. Further, it unclear what constitutes as inaccessible to the first entity (the first entity encompassing any person associated with the computer). For example, is the second dataset inaccessible to the first entity because the first entity is too far from where the second dataset is stored or is the second dataset inaccessible to the first entity because the first entity is restricted from obtaining the second dataset. Therefore, it is unclear what constitutes as inaccessible. The specification does not provide a clear and precise definition of the limitation, nor would one skilled in the art recognize the metes and bounds of said limitation. Dependent claims 2-17 and 19-31 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, this limitation will be interpreted as an intended use of the transformation step.
Claim 1 recites the limitation “the new subject” in line 17 of the claim which renders the metes and bounds of the claim indefinite. There is insufficient antecedent basis for this limitation in the claim. The indefiniteness arises because it is unclear what “the new subject” is referring to. Dependent claims 2-17 and 19-31 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, this limitation will be interpreted as “a new subject” which will also provide proper basis for “the new subject” recited in line 23 of claim 1, claim 7, claim 8, and claim 13.
Claim 1 recites “correcting the first dataset based on the adaptation factors” which renders the metes and bounds of the claim indefinite. The indefiniteness arises because it is unclear if “the adaptation factors” is referring to the adaptation factors in the “first set of adaptation factors”, if “the adaptation factors” is referring to the adaptation factors in the “second set of adaptation factors”. Dependent claims 2-17 and 19-31 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, “the adaptation factors” will be interpreted as the adaptation factors in the first set of adaptation factors.
The term “high-expression” in claim 19 is a relative term which renders the claim indefinite. The term “high-expression” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The genes which are being referred to in the claims are rendered indefinite by the use of the term “high-expression” because it is unclear to what degree are is a gene considered as a high-expression gene. The specification does not provide a clear and precise definition of the limitation, nor would one skilled in the art recognize the metes and bounds of said limitation. For the sake of furthering examination, the limitation of “high-expression genes” will be interpreted as “genes”.
Claims 21 and 25 recite “the RNA expression data” which renders the metes and bounds of the claim indefinite. The indefiniteness arises because it is unclear if “the RNA expression data” is referring to the “first set of RNA expression data” or the “second set of RNA expression data”. Dependent claims 22-24 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, “the RNA expression data will be interpreted as referring to the “first set of RNA expression data”.
Claims 27 recites “wherein a training dataset of the machine learning molecular model comprises both a subset of the first set of RNA expression data and a subset of the second set of RNA expression data” and claim 28 recites “wherein a training dataset of the machine learning molecular model comprises a subset of a third set of RNA expression data generated from a third set of sequencing equipment” which renders the metes and bounds of the claim indefinite. The indefiniteness arises because the independent claim recites generating the machine learning molecular model is trained by the first computer (which is associated with the first entity) from the transformed first set of RNA expression data which makes it unclear if untransformed data from different RNA expression datasets are also used in the training process. Further, the independent claim recites that the second data set is inaccessible to the first entity (the computer associated with the first entity is the one generating the model) which further makes it unclear if the second set of RNA expression data in this training dataset is the same as the RNA expression dataset in the independent claim. Dependent claim 29 is rejected by virtue of its dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, these claims will be interpreted as using subsets of RNA expression data for training.
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 rejection below has been modified in view of the amendments.
Claims 1-17 and 19-31 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) without significantly more.
(Step 1)
Claims 1-17 and 19-31 fall under the statutory category of a process.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are abstract ideas of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations. The instant claims also recite judicial exceptions that is in the grouping of a “law of nature” such as a naturally occurring correlation.
Independent claim 1 recites mathematical concepts and mental processes of transforming the first set of RNA expression data based at least in part on characteristics of a second dataset having a second set of RNA expression data, the transforming comprising reducing a dimensionality of the first and second datasets to generate first and second reduced datasets, generating a first set of adaptation factors related to the first reduced dataset, generating a second set of adaptation factors related to the second reduced dataset, and correcting the first dataset based on the adaptation factors, wherein at least one of the first set of sequencing equipment or methods used to generate the first set of RNA expression data from the first set of sequencing equipment introduce a dataset specific nature into the first set of RNA expression data that is not represented in the second dataset, and wherein the second dataset is inaccessible by the first entity, generating a machine learning molecular model trained from the transformed first set of RNA expression data, providing the RNA expression data of the record to the generated machine learning molecular model, and generating using the generated machine learning molecular model and based on the RNA expression data of the record a predicted classification of the new subject’s event occurrence.
Dependent claim 7 recites mental processes and laws of nature of predicting a subject’s outcome to treatments based at least in part on the prediction of the new subject’s risk of cancer recurrence or predicting the new subject’s risk of cancer recurrence based at least in part on the classification of the new subject’s response to treatment. Dependent claim 10 recites mathematical concepts and mental processes of generating a transform between the first set of adaptation factors and the second set of adaptation factors, encoding two or more corresponding eigenvalues of the second dataset with the generated transform, and providing the encoded two or more corresponding eigenvalues of the second dataset as the adapted second data set. Dependent claim 12 recites mathematical concepts and mental processes of transforming RNA expression data of the second record based at least in part on characteristics of the first set of RNA expression data, providing the transformed RNA expression data of the second record to the generated machine learning molecular model, and generating a predicted classification of the second subject’s event occurrence, wherein the second new subject’s event occurrence is a cancer recurrence or a response to treatment. Dependent claim 12 further recites a law of nature of a correlation RNA expression data and a subject’s risk of cancer recurrence or a response to treatment.
The claims recite mental processes of analyzing RNA expression data using mathematical calculations to process numerical data representing RNA expression of transforming RNA expression data, generating a machine learning molecular model (which encompasses a linear regression process and logistic regression process), providing RNA expression data to the generated machine learning molecular model, and generating using the generated machine learning molecular model and based on the RNA expression data a predicted classification of the new subject’s event occurrence. The human mind is capable of performing mathematical calculations on abstract numerical data (that represents RNA expression data) to process numerical data. The claims recite mathematical concepts that are mathematical calculations. The MPEP states at 2106.04(a)(2)(I)(C) that “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”.
The BRI of the claimed limitations indicated above as reciting mathematical concepts encompass performing mathematical calculations.
The limitations of transforming a RNA expression dataset based on characteristics of a different RNA expression dataset by performing dimensionality reduction, generating adaptation factors, and correcting a RNA expression dataset based on the adaptation factors are mathematical concepts because these steps encompass mathematical calculations. The instant disclosure provides that dimensionality reduction is performed by principal component analysis which is a series of mathematical calculations to generate principal components (i.e., eigenvectors/ adaptation factors of the covariance matrix of the RNA expression datasets covariance matrix) and an encoded values from the dataset (i.e., reduced RNA expression datasets) (see instant disclosure [0127] and [0134]). The instant disclosure provides that correcting a RNA expression dataset based on the adaptation factors is performed by a gradient descent optimization process which minimizes the Euclidean distance between factors of different data sets (i.e., the Euclidian distance between eigenvectors of a first RNA expression data covariance matrix and eigenvectors of a second RNA expression data covariance matrix) which is a series of mathematical calculations (see instant disclosure [0088], [0098], [0128] and [0135]).
The instant disclosure provides that generating a machine learning molecular model trained from the transformed first set of RNA expression data encompasses using a linear regression algorithm or logistic regression algorithm (see instant disclosure [0085]) which fits linear regression (or logistic regression) equations to the data using a series of mathematical calculations to produce a final fitted mathematical equation with optimal coefficients (i.e., the machine learning molecular model itself encompasses a fitted mathematical equation with optimal coefficients). Therefore, providing the RNA expression data of the record to the generated machine learning molecular model and generating a predicted classification of the new subject’s event occurrence encompasses providing numerical data which represents RNA expression data to a fitted mathematical equation with optimal coefficients produced by a linear regression algorithm (or logistical regression algorithm) to produce a numerical output representative of a classification of the new subjects event occurrence which are mathematical calculations (see instant disclosure [0085] for discussion on the machine learning algorithms).
Dependent claims 2-6, 8, 9, 11, 13-17, and 19-31 further limit the mental process/mathematical concept recited in the independent claim but do not change their nature as a mental process/mathematical concept. Claims 2-6, 9, 11, 14-17, 19-30 further recite limitations about the abstract data that is being processed by the judicial exceptions. It is noted that further limiting abstract data which is processed by mathematical calculations does not change the nature of the mathematical concepts. Further, claims 7, 8, 12, 13, and 31 recite a law of nature for reciting the event occurrence is a risk of cancer recurrence or a response to treatment which is a recitation of a naturally occurring correlation between a subject’s RNA expression and cancer recurrence or response to a treatment.
(Step 2A Prong 2)
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional element in claims 1-6, 9, 11, 12, 14-26, and 30 of receiving data does not integrate the judicial exception into a practical application because this is adding insignificant extra solution activity of data gathering. Claims 2-6, 9, 11, 14-26, and 30 limit the content of the data being received, they do not change the receiving step from being a step of data gathering because the content of data does not change the character of what is occurring in the additional element process of receiving data in a computer environment.
The additional element in claims 1 of using a generic computer to perform judicial exceptions (i.e., using a first computer system with an artificial intelligence engine) does not integrate the judicial exception into a practical application because this applying the judicial exception to a generic computer without an improvement to computer technology (see MPEP 2106.04(d)(1))
Thus, the additional elements do not integrate the judicial exceptions into a practical application and claims 1-17 and 19-31 are directed to the abstract idea.
(Step 2B)
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
The additional element in claims 1-6, 9, 11, 12, 14-26, and 30 of receiving data is conventional see MPEP 2106.05(b) and 2106.05(d)(II). Claims 2-6, 9, 11, 14-26, and 30 limit the content of the data being received, they do not change the receiving step from being a step of data gathering because the content of data does not change the character of what is occurring in the additional element process of receiving data in a computer environment.
The additional element in claims 1 of using a generic computer to perform judicial exceptions is conventional see MPEP 2106.05(b) and 2106.05(d)(II).
The combination of receiving data and using a generic computer to perform judicial exceptions is conventional see MPEP 2106.05(b) and 2106.05(d)(II).
Thus, the additional elements are not sufficient to amount to significantly more than the judicial exception because they are conventional see MPEP 2106.05(b) and 2106.05(d)(II).
Response to Arguments
Applicant's arguments filed 20 March 2025 have been fully considered but they are not persuasive.
Argument 1:
Applicant argues that the limitations of “generated from a second set of sequencing equipment” and “wherein at least one of the first set of sequencing equipment or methods used to generate the first set of RNA expression data from the first set of sequencing equipment introduce a dataset specific nature into the first set of RNA expression data that is not represented in the second dataset” have not been addressed in the previous Office actions. Applicant further argues that the response provided in the last action stating that the contents of the data being received does not change the additional element of receiving does not address these limitations because these limitations are part of the transforming step (Reply p. 9).
These arguments have been fully considered but found to be not persuasive. It is noted that these limitations are part of the abstract idea because the content of the data being processed by the judicial exceptions is abstract (i.e., numerical data representing RNA expression data which was produced in a particular manner). Further, this limitation is interpreted as a product by process limitation and does not require the active steps of performing sequencing to generate the RNA expression data. For clarity, in the 101 analysis above the step previously indicted as “transforming the first set of RNA expression data…” is no longer truncated and is provided in its entirety to show these limitations are part of the abstract idea.
Argument 1 (Step 2A, Prong 1):
Applicant argues that the step of “transforming the first set of RNA expression data based at least in part on characteristics of a second dataset having a second set of RNA expression data generated from a second set of sequencing equipment,… wherein at least one of the first set of sequencing equipment or methods used to generate the first set of RNA expression data from the first set of sequencing equipment introduce a dataset specific nature into the first set of RNA expression data that is not represented in the second dataset” is not a mathematical calculation because the office action does not identify any such calculation with respect to this transforming step. Applicant argues that this limitation at most “involves a broad array of techniques and/or activities that may involve or rely upon mathematical concepts (Reply p. 9-11). Applicant further argues that this transforming step is not a mental process because the human mind is cannot practically perform this step. Applicant further argues that the understanding of this dataset specific nature, how it renders the nature of the first set of RNA expression data different from the nature of the second set of RNA expression data, and how the former needs to be transformed in view of the latter is not something that can be done practically in the human mind (Reply p. 10).
These arguments have been fully considered but found to be not persuasive. The MPEP states at 2106.04(a)(2)(I)(C) that “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”.
As stated above in the analysis, the instant disclosure provides that dimensionality reduction is performed by principal component analysis which is a series of mathematical calculations to generate principal components (i.e., eigenvectors/ adaptation factors of the covariance matrix of the RNA expression datasets covariance matrix) and an encoded values from the dataset (i.e., reduced RNA expression datasets) (see instant disclosure [0088], [0098], [0127] and [0134]). The instant disclosure provides that correcting a RNA expression dataset based on the adaptation factors is performed by a gradient descent optimization process which minimizes the Euclidean distance between factors of different data sets (i.e., the Euclidian distance between eigenvectors of a first RNA expression data covariance matrix and eigenvectors of a second RNA expression data covariance matrix) which is a series of mathematical calculations (see instant disclosure [0128] and [0135]). The BRI of the transforming limitations in light of the specification encompass a series of mathematical calculations and therefore recite mathematical calculations.
As described above, the transformation encompasses a series of mathematical equations to process abstract numerical data (which represents RNA expression data). The human mind is capable of performing a series of mathematical equations to process abstract numerical data (such as performing Principal Component Analysis) and more specifically the human mind is capable of understanding the differences in dataset specific nature between multiple datasets by analyzing the covariance matrices of datasets, eigenvalues of these covariance matrices, and eigenvectors (or principal components) of these covariance matrices. Further, the human mind is capable of minimizing the Euclidian distance between eigenvectors of the first RNA expression dataset and the eigenvectors of the second RNA expression dataset with a series of mathematical calculations to correct the dataset specific nature which is identified by the eigenvectors. The human mind is capable of performing these mathematical analyses (which is a series of calculations) on abstract numerical data which represents RNA expression.
Argument 2 (Step 2A, Prong 1):
Applicant argues that the step of “generating… a machine learning molecular model” is not a mathematical concept (Reply p. 11-12). Applicant points to the Kim Memo which distinguishes Office eligibility examples 39 and 47 on the basis that the latter refers to the mathematical calculation by name, i.e., a backpropagation algorithm and a gradient descent algorithm while the former “does not set fort or describe” any such calculations. Applicant argues that the claims do not recite a linear regression algorithm and therefore do not recite a mathematical concept (Reply p. 11-12). Applicant further argues the step of “generating… a machine learning molecular model” does not recite a mental process because it is not a process of analyzing/evaluating data and applicant argues that the previous arguments regarding fitting a linear regression model are inapplicable because linear regression is not machine learning even if a machine learning model may rely on linear regression (Reply p. 11).
This argument has been fully considered but found to be not persuasive. The instant claims have a different fact pattern then the claims in examples 39 and 47 as discussed in the Kim Memo. The claims in the examples are directed to neural network training and when it arises to reciting math. In contrast the instant claims recite a generic machine learning molecular model and in light of the specification this machine learning molecular model encompasses generating a linear regression model using a linear regression algorithm which encompasses fitting a linear regression algorithm with numerical data to build a linear regression equation/model with optimal coefficients that best fits the data. The MPEP states at 2106.04(a)(2)(I)(C) that “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”.
As stated above in the analysis above, the instant disclosure provides that generating a machine learning molecular model trained from the transformed first set of RNA expression data encompasses using a linear regression algorithm or logistic regression algorithm (see instant disclosure [0085]) which fits linear regression (or logistic regression) equations to the data using a series of mathematical calculations to produce a final fitted mathematical equation with optimal coefficients (i.e., the machine learning molecular model itself encompasses a fitted mathematical equation with optimal coefficients). Therefore, providing the RNA expression data of the record to the generated machine learning molecular model and generating a predicted classification of the new subject’s event occurrence encompasses providing numerical data which represents RNA expression data to a fitted mathematical equation with optimal coefficients produced by a linear regression algorithm (or logistical regression algorithm) to produce a numerical output representative of a classification of the new subjects event occurrence which are mathematical calculations (see instant disclosure [0085] for discussion on the machine learning algorithms). Therefore, in the instant case the limitation of generating a machine learning molecular model encompasses a process which is a series of mathematical calculations and thus is a mathematical concept.
As described above, the machine learning model encompasses a linear regression algorithm (or logistical regression algorithm) which performs linear regression on abstract numerical data (which represents RNA expression data) to generate a model which encompasses fitting linear regression equations to data using a series of mathematical equations to produce a fitted linear equation with optimal coefficients. The human mind is capable of fitting a linear regression equation (which is the model to be fitted in the in the linear regression machine learning algorithm) using abstract numerical data and mathematical processes to generate a linear regression equation (the model) with optimal coefficients. It is noted that a linear regression algorithm is a supervised machine learning algorithm that works by fitting a linear relationship between input features and output features and providing a linear equation which best fits the linear relationship. The human mind is capable of fitting a linear relationship represented as a linear equation. Thus, the human mind is capable of generating a machine learning molecular model because it encompasses a fitting a linear regression model based on input features and output features.
Argument 1 (Step 2A, Prong 2 and Step 2B):
Applicant further argues the steps of “transforming the first set of RNA expression data…” and “generating a molecular model” are not mathematical calculations and should be viewed as additional elements (Reply p. 13).
This argument has been fully considered but found to be not persuasive. As stated above the steps of “transforming the first set of RNA expression data…” and “generating a molecular model” fall under the judicial exceptions because they are mathematical calculations.
Argument 2 (Step 2A, Prong 2 and Step 2B):
Applicant argues the office practical application analysis improperly analyzes only the additional elements of the claims and not the claim “as a whole,” it did not engage in the proper analysis, such that it did not establish a prima facie case of alleged unpatentability (Reply p. 16).
This argument has been fully considered but found to be not persuasive. The MPEP states at 2106.04(d)(III) “the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception need to be evaluated together to determine whether the claim integrates the judicial exception into a practical application. Because a judicial exception alone is not eligible subject matter, if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. However, the way in which the additional elements use or interact with the exception may integrate it into a practical application” which shows the additional elements provide the practical application, either on their own or in combination with the judicial exceptions.
As stated in the rejection above, the additional elements are a generic computer and receiving data. The rejection goes on to state that the use of a generic computer to perform judicial exceptions do not integrate them into a practical application because there is no improvement to computer functionality (i.e. the abstract ideas are using a computer as a tool). Further, the step of receiving data interacts with the judicial exceptions only to provide data for the judicial exceptions to process which is the reason this step was found to be insignificant extra solution activity of data gathering. The claim has been considered as a whole because the additional elements and their relationship with the judicial exceptions has been considered.
Argument 3 (Step 2A, Prong 2 and Step 2B):
Applicant argues under a proper analysis, Applicant’s claims properly integrate any alleged exception into a practical application by reflecting an improvement to the function of a computer, technology, or technical field. Applicant points to the “Precedential” decision Ex parte Desjardins which found that an improvement to how the machine learning mode itself operates is an improvement in machine learning technology. Applicant argues that the transforming and generating steps also reflect logical structures and processes that reflect an improvement in the operation of the artificial intelligence engine implementing the generated machine learning model and the claims are “necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computers” (Reply p. 14-15).
This argument has been fully considered but found to not be persuasive. Ex parte Desjardins has a different fact pattern than the instant case. The claims in Ex parte Desjardins are directed to specific limitations of adjusting parameters of a machine learning model in a particular way during training which was found to solve the technical problem of “catastrophic forgetting” which is a technical problem in multi-task learning with neural network models. It was found that the claims in Ex parte Desjardins were directed to a specific training process that allowed a machine learning model to learn new tasks while protecting knowledge about previous tasks which improved the machine learning model itself. In contrast the instant claims do not provide a specific training process to generate the machine learning molecular model in a manner where the machine learning model itself is improved, the instant claims only provide that the model is generated by training on transformed RNA expression data. Training on a particular dataset is not an improvement in the machine learning model itself and correcting for batch effects in data using transformations (the transformations themselves being mathematical calculations as described above) is problem in abstract data analysis itself when making predictions using multiple datasets with different biases. Therefore, the claims do not recite an improvement to machine learning models themselves and the argued improvement is provided by and realized in the abstract idea of analyzing data using multiple datasets with different biases which is part of the abstract idea. It is noted that the MPEP provides that “It is important to note, the judicial exception alone cannot provide the improvement” (MPEP 2106.05(a)).
Argument 4 (Step 2A, Prong 2 and Step 2B):
Applicant argues that claim 1 has been amended to recite a detailed mechanism for implementing the recited transformation step and it is not merely generic and provides a particular solution to a problem (Reply p. 16).
This argument has been fully considered but found to be not persuasive. As described in the rejection above all transformation steps and the contents of the data used int eh transformation steps are part or the judicial exception itself because it is a series of mathematical calculations. Thus, these steps cannot provide significantly more than the abstract idea because they themselves are part of abstract idea (i.e., they are not additional elements).
Claim Rejections - 35 USC § 103
The rejection on the ground of 103 of claims 1, 4-6, 8, 11-20, 26-28, and 30 as being unpatentable over Haghverdi et al. (Nat Biotechnol 36, 421–427 (2018); previously cited) in view of Kourou et al. (Comput Struct Biotechnol J. 2014 Nov 15;13:8-17; previously cited) in Office action mailed 29 July 2025 is withdrawn in view of the amendment of “the transforming comprising reducing a dimensionality of the first and second datasets to generate first and second reduced datasets, generating a first set of adaptation factors related to the first reduced dataset, generating a second set of adaptation factors related to the second reduced dataset, and correcting the first dataset based on the adaptation factors” received 01 December 2025.
The rejection on the ground of 103 of claims 2 and 3 as being unpatentable over Haghverdi et al. in view of Kourou et al. as applied to claims 1, 4-6, 8, 11, 12, 15-20, 26-28, and 30 and in further in view of Castillo et al. (PloS one 14.2 (2019): e0212127; previously cited) in Office action mailed 29 July 2025 is withdrawn in view of the amendment of stated above received 01 December 2025.
The rejection on the ground of 103 of claims 9 as being unpatentable over Haghverdi et al. in view of Kourou et al. as applied to claims 1, 4-6, 8, 11, 12, 15-20, 26-28, and 30 and in further in view of Jacobs et al. (Int. J. Cancer, 120: 67-74; previously cited) in Office action mailed 29 July 2025 is withdrawn in view of the amendment of stated above received 01 December 2025.
The rejection on the ground of 103 of claims 10 as being unpatentable over Haghverdi et al. in view of Kourou et al. as applied to claims 1, 4-6, 8, 11, 12, 15-20, 26-28, and 30 and in further in view of Buttner et al. (Nature methods 16.1 (2019): 43-49; previously cited) in Office action mailed 29 July 2025 is withdrawn in view of the amendment of stated above received 01 December 2025.
The rejection on the ground of 103 of claims 21, 22, 24, 25, and 29 as being unpatentable over Haghverdi et al. in view of Kourou et al. as applied to claims 1, 4-6, 8, 11, 12, 15-20, 26-28, and 30 and in further in view of Kim et al. (Bioinformatics, Volume 36, Issue 5, March 2020, Pages 1360–1366; previously cited) in Office action mailed 29 July 2025 is withdrawn in view of the amendment of stated above received 01 December 2025.
The rejection on the ground of 103 of claims 23 as being unpatentable over Haghverdi et al. in view of Kourou et al. in view of Kim et al. as applied to claims 21, 22, 24, 25, and 29 and in further in view of Jee et al. (Mol. Carcinog., 54: 1605-1612; previously cited) in Office action mailed 29 July 2025 is withdrawn in view of the amendment of stated above received 01 December 2025.
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 rejection below is newly recited. It is noted that Korsunsky et al. (Nat Methods 16, 1289–1296 (2019); newly recited) provides a Methods section at the end of the paper with no page numbers. There is 5 pages of this Methods section (each page in this section is referred to as page 1 of Methods, page 2 of Methods, etc.).
Claims 1-17 and 19-31 are rejected under 35 U.S.C. 103 as being unpatentable over Sakellaropoulos et al. (Cell Reports, Volume 29, Issue 11, 3367 - 3373.e4 (2019); newly recited) in view of Korsunsky et al. (Nat Methods 16, 1289–1296 (2019); newly recited).
Claim 1 is directed to receiving, at a first computer system associated with a first entity, a first dataset having a first set of RNA expression data generated from a first set of sequencing equipment,
Sakellaropoulos et al. shows receiving gene expression of GDSC cell lines form the Array Express repository which was generated from a first set of sequencing equipment (Sakellaropoulos et al. page e1 section “Gene expression cell line and patient datasets”).
transforming the first set of RNA expression data based at least in part on characteristics of a second dataset having a second set of RNA expression data generated from a second set of sequencing equipment,
Sakellaropoulos et al. shows performing batch correction between GDSC cell line data and clinical datasets to correct for batch effects (Sakellaropoulos et al. page e2). Sakellaropoulos et al. shows the GDSC cell line data and clinical datasets are gathered from different sequencing equipment such as microarray sequencing equipment and Next generation RNA-seq sequencing equipment (Sakellaropoulos et al. page e1-e2).
generating a molecular model trained from the transformed first set of RNA expression, receiving a record associated with the new subject the record having RNA expression data generated from the second set of sequencing equipment, providing the RNA expression data of the record to the generated model and generating a predicted classification of the new subject’s risk of event occurrence.
Sakellaropoulos et al. shows training machine learning models Deep Neural Network only on the cell line gene expression data (after batch correction between the cell line and clinical datasets) to determine the machine learning models optimal models (Sakellaropoulos et al. page 3368 right col. and page e2). Sakellaropoulos et al. shows the optimal models were utilized to predict z-score normalized IC50 values for each patient in the clinical dataset from the patient’s gene expression data which is interpreted as an event occurrence of a response to treatment (Sakellaropoulos et al. page e2).
Sakellaropoulos et al. does not show the transforming comprising reducing a dimensionality of the first and second datasets to generate first and second reduced datasets, generating a first set of adaptation factors related to the first reduced dataset, generating a second set of adaptation factors related to the second reduced dataset, and correcting the first dataset based on the adaptation factors, wherein at least one of the first set of sequencing equipment or methods used to generate the first set of RNA expression data from the first set of sequencing equipment introduce a dataset specific nature into the first set of RNA expression data that is not represented in the second dataset, and wherein the second dataset is inaccessible by the first entity
Like Sakellaropoulos et al., Korsunsky et al. shows a batch correction method for RNA expression datasets. Korsunsky et al. shows a process of performing batch correction by using principal component analysis (PCA) to embed cells (represented by gene expression data) into a space with reduced dimensionality (Korsunsky et al. page 1290 Figure 1). Korsunsky et al. shows that the process uses eigenvalue-scaled eigenvectors produced by the principal component analysis (which is interpreted as adaptation factors) as the low-dimensional embedding for batch correction (Korsunsky et al. “Harmony” section page 1 left col. of Methods). Korsunsky et al. shows correcting datasets (i.e. batch correction process) which is based on the low-dimensional embedding (eigenvalue-scaled eigenvectors/the adaptation factors) (Korsunsky et al. page 1290 Figure 1, “Harmony” section page 1 left col. of Methods, and “Linear mixture model correction” section on pages 3-4 of Methods). Korsunsky et al. the batch correction process is utilized for batch correction between different sequencing equipment (“Analysis details” section page 4 right col. of Methods). The limitation “wherein the second dataset is inaccessible by the first entity” is interpreted as an intended use of the transformation step (see 112/b rejection above).
Dependent claim 2 is directed to wherein the first sequencing equipment comprises microarray sequencing equipment and the second sequencing equipment comprise next generation sequencing equipment. Dependent claim 3 is directed to wherein the first sequencing equipment comprises next generation sequencing equipment and the second sequencing equipment comprise microarray sequencing equipment.
Sakellaropoulos et al. in view of Korsunsky et al. shows utilizing data produced by microarray sequencing equipment and next generation sequencing equipment (Sakellaropoulos et al. page e1-e2). Further, Sakellaropoulos et al. in view of Korsunsky et al. shows the ability of performing batch correction between different sequencing equipment technology (Korsunsky et al. page 4 right col. of Methods).
Dependent claim 4 is directed to wherein the first dataset is a public dataset and the second dataset is a laboratory specific dataset. Dependent claim 5 is directed to wherein the first dataset is a laboratory-specific dataset and the second dataset is a public dataset. Dependent claim 6 is directed to wherein the first dataset is a laboratory-specific dataset and the second dataset is a second laboratory specific dataset.
Sakellaropoulos et al. in view of Korsunsky et al. shows the use of RNA expression datasets produced from different laboratories (Sakellaropoulos et al. page e1 section “Gene expression cell line and patient datasets”) and shows the use of RNA expression datasets from public databases (Sakellaropoulos et al. page e1 section “Gene expression cell line and patient datasets”). Sakellaropoulos et al. in view of Korsunsky et al. shows that the data from the different labs are also public data from databases (Sakellaropoulos et al. page e1 section “Gene expression cell line and patient datasets”).
Dependent claim 9 is directed to wherein the first dataset is a germline dataset from a laboratory in a first location and the second dataset is a germline dataset from a laboratory in a second location. Dependent claim 11 is directed to wherein the record further comprises DNA mutation data generated from the second set of sequencing equipment. Claim 26 is directed to wherein the record further comprises pathology imaging features from a pathology slide.
Sakellaropoulos et al. in view of Korsunsky et al. shows gathering TCGA datasets from a reference which includes DNA variants from datasets such as germline variants (Sakellaropoulos et al. page e2 section TCGA dataset). (Sakellaropoulos et al. in view of Korsunsky et al. shows a OCCAMS dataset was gathered for prediction which includes corresponding histological response data (Sakellaropoulos et al. page e2).
Claim 10 is directed wherein correcting the first dataset based on the adaptation factors comprises generating a transform between the first set of adaptation factors and the second set of adaptation factors, encoding two or more eigenvalues of the second dataset with the generated transform, and providing the eigenvalues of the second dataset as the adapted second dataset.
Sakellaropoulos et al. in view of Korsunsky et al. shows generating a transform between the first set of adaptation factors and the second set of adaptation factors by generating cell-specific correction values which is a linear combination of dataset correction factors weighted by the cell’s soft cluster assignments made and further shows a process of reference mapping where query datasets can be mapped onto a reference dataset (Korsunsky et al. page 1290 Figure 1 and page 3 right col of Methods). Sakellaropoulos et al. in view of Korsunsky et al. shows cells are embedded into a low-dimensional space as a result of PCA analysis by performing PCA on high-dimensional gene expression matrix and use the eigenvalue-scaled eigenvectors as the low dimensional embedding input for batch correction (Korsunsky et al. page 1 left col. of Methods). Sakellaropoulos et al. in view of Korsunsky et al. shows multiplying the cell embeddings by the eigenvalues to avoid given eigenvectors equal variance (i.e., eigenvalue-scaled eigenvectors) (Korsunsky et al. page 4 right col. of Methods).
Dependent claim 12 is directed to receiving a record form a second new patient with RNA expression data generated from a third set of sequencing equipment, transforming the RNA expression data and providing the transformed RNA expression data to the model to predict classification of the second new subjects event occurrence, wherein the second new subject’s event occurrence is a cancer recurrence or a response to treatment.
Sakellaropoulos et al. in view of Korsunsky et al. shows the optimal models were utilized to predict z-score normalized IC50 values for each patient in the clinical dataset from the patient’s gene expression data which is interpreted as an event occurrence of a response to treatment (Sakellaropoulos et al. page e2). This shows that the steps are repeated for each patient in the clinical dataset to predict the response to treatment
Dependent claim 14 is directed to wherein the first set of sequencing equipment and the second set of sequencing equipment sequences up to 140,000 transcripts. Dependent claim 15 is directed to wherein the first set of sequencing equipment and the second set of sequencing equipment sequences between 10 genes and 20,000 genes.
The BRI of these claims do not recite an active step of sequencing up to 140,000 transcripts or sequencing between 10 and 20,000 genes and is a product by process limitation of how the sequencing equipment produces the first and second datasets. Sakellaropoulos et al. in view of Korsunsky et al. shows the datasets include transcripts and sequences from 16,445 genes (Sakellaropoulos et al. page 3369 Figure 2).
Dependent claim 16 is directed to wherein characteristics of first set of sequencing equipment sequences have differences from the second set of sequencing equipment sequences. Dependent claim 17 is directed to wherein the characteristics are measured by variance of each gene or by heterogeneity. Dependent claim 19 is directed to wherein the characteristics are measured across high-expression genes. Dependent claim 20 is directed to wherein the characteristics occur in at least one of the FGFR2, MAP3K1, TNRC9, BRCA1, and BRCA 2 genes.
The BRI of these claims do not recite an active step of measuring variance of each gene, measuring heterogeneity, or measuring across high-expression genes, they are descriptive of how the underlying characteristics differences can be identified but are not active steps of the method or limitations to what the characteristics are (just how they may be measured). Sakellaropoulos et al. in view of Korsunsky et al. shows that the GDSC cell line data and clinical datasets are gathered from different sequencing equipment such as microarray sequencing equipment and Next generation RNA-seq sequencing equipment (Sakellaropoulos et al. page e1-e2). Sakellaropoulos et al. in view of Korsunsky et al. further shows variation in the ERK MAPK signaling which includes the MAP3K1 gene (Sakellaropoulos et al. page 3369 Figure 2). It is interpreted that due to the difference in the technological equipment used to gather the data the characteristic between sequences will be different and will occur in all genes sequenced.
Dependent claim 21 is directed to wherein the RNA expression data is from gene expression across different cancer types. Dependent claim 25 is directed to wherein the RNA expression data is from a tumor cell.
Sakellaropoulos et al. in view of Korsunsky et al. shows utilizing RNA expression data from cancer-cell lines acquired from the Genomics of Drug Sensitivity in Cancer (GDSC) database which includes cancer cell-lines across different cancer types (Sakellaropoulos et al. page 3368 and page 3369 Figure 2A).
Dependent claim 22 is directed wherein the different cancer types comprise at least two of breast, colorectal, pancreatic, lung, and bladder cancers. Dependent claim 23 is directed to wherein the different cancer types comprise at least two of squamous and immunogenic. Dependent claim 24 is directed to wherein the different cancer types comprise adenocarcinoma and neuroendocrine cancers.
Sakellaropoulos et al. in view of Korsunsky et al. shows that the different cancer types include breast, bladder, lung, and pancreatic (Sakellaropoulos et al. page 3369 Figure 2A). Sakellaropoulos et al. in view of Korsunsky et al. further shows the different cancer types also include Lung NSCLC squamous cell carcinoma and Melanoma (which is interpreted as an immunogenic cancer) (Sakellaropoulos et al. page 3369 Figure 2A). Sakellaropoulos et al. in view of Korsunsky et al. further shows the different cancer types also include Lung NSCLC adenocarcinoma and Lung small cell carcinoma (which is a type of neuroendocrine cancer) (Sakellaropoulos et al. page 3369 Figure 2A).
Dependent claims 27 and 28 are directed to using subsets of RNA expression data for training. Dependent claim 29 wherein the training dataset excludes gene variants which are not informative to the machine molecular model.
Sakellaropoulos et al. in view of Korsunsky et al. shows using 5-fold cross validation for training the machine learning model which splits the RNA expression data into subsets for training (Sakellaropoulos et al. page e2). Sakellaropoulos et al. in view of Korsunsky et al. shows selecting highly variable genes for training the machine learning model which is interpreted as excluding gene variants that are not informative ((Sakellaropoulos et al. page e2).
Dependent claim 30 is directed to wherein the dataset specific nature reflects a bias.
Sakellaropoulos et al. in view of Korsunsky et al. shows batch performing batch correction which corrects for batch effects which are differences between characteristics in datasets which is a bias (Sakellaropoulos et al. page e2 and Korsunsky et al. page 1 left col. “Methods”).
Dependent claim 31 is directed to wherein the event occurrence is a cancer recurrence or a response to treatment. Dependent claim 7 is directed to predicting a subject’s outcome to treatments based at least in part on the prediction of the new subject’s risk of cancer recurrence or predicting the new subject’s risk of cancer recurrence based at least in part on the classification of the new subject’s response to treatment. Dependent claim 8 is directed to wherein the prediction of the new subject’s risk of cancer recurrence is based at least in part on a cancer type or cancer subtype, or wherein the classification of the new subject’s response to treatment comprises predicting a high-risk of a cancer type. Dependent claim 13 is directed to wherein the classification of the new subject’s risk of cancer recurrence comprises an estimate on a duration of time until the new subject’s risk of cancer recurrence occurs or wherein the classification of the new subject’s response to treatment further comprises an estimate on a duration of time until the new subject’s response to treatment occurs.
Sakellaropoulos et al. in view of Korsunsky et al. shows the optimal models were utilized to predict z-score normalized IC50 values for each patient in the clinical dataset from the patient’s gene expression data which is interpreted as an event occurrence of a response to treatment (Sakellaropoulos et al. page e2). Sakellaropoulos et al. in view of Korsunsky et al. performing Kaplan-Meyer survival analysis to contrast the groups of the lowest and highest IC50 (Sakellaropoulos et al. page e3). Sakellaropoulos et al. in view of Korsunsky et al. shows Kaplan-Meyer survival plots for specific drugs in different cohorts with time under treatment in days (for Bortezomib, Paclitaxel, and Cisplatin-TCGA) and weeks (for PARP-Inhibitor, and Cisplatin-OCCAMS) on the x-axis and the ratio of the surviving patients on the y-axis (Sakellaropoulos et al. Supplementary Figure 2.). It is implicitly shown that response to treatment is related to cancer recurrence in patients who have a beneficial response initially but then respond less which leads to a decrease patient survival (Sakellaropoulos et al. Supplementary Figure 2e (patients treated with Paclitaxel) which shows a steady patient survival for a period of time in the cohort with Low IC50 values followed by a steep decrease in patient survival which indicates less response to treatment/less effective of minimizing cancer cell growth (in the context of Paclitaxel which is an agent to stop cancer cell growth). This shows the relationship between response to treatment and survival rates over a duration of treatment time.
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have modified the batch correction process of Sakellaropoulos et al. with the particular batch correction method as shown in Korsunsky et al. because this would allow for method to use of a batch correction process that is computationally efficient, requires less memory compared to other algorithms, and is capable of analyzing large datasets on personal computers (Korsunsky et al. page 1290 right col.). One would have a reasonable expectation of success for this modification because Sakellaropoulos et al. shows the use of a batch correction process to correct for batch effects while Korsunsky et al. shows a computationally efficient batch correction process to correct for batch effects.
Response to Arguments
Applicant's arguments filed 01 December 2025 have been fully considered but they are not persuasive.
Applicant arguments are directed to previously cited references of Haghverdi et al. and Kourou et al. however Haghverdi et al. and Kourou et al. are no longer relied upon for the 103 rejection above. Therefore, the arguments recited are not persuasive.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action.
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/J.E.H./Examiner, Art Unit 1685
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685