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 . This action is made final.
This action is in response to the claims filed May 26, 2026. Claims 1, 15, and 29 have been amended.
Response to Amendment
The amendments filed May 26 2026 have been entered. Claims 1-3, 7-12, 15-17, 21-26, 29-31, 35-40, and 43 remain pending in the case and have been examined. Claims 1-3, 7-12, 15-17, 21-26, 29-31, 35-40, and 43 are rejected.
Response to Arguments
Regarding the 101 Arguments
Applicant's arguments filed May 26, 2026 have been fully considered but they are not persuasive.
Applicant argues on pages 10-11
Applicant respectfully submits that the Step 2A, Prong 1 analysis of the claims in the Office Action is improper, as it fails to consider the recent guidance directed to present Examiner's Art Unit (i.e., Art Unit 2100) cautioning Examiners against grouping claims related to training of machine learning models into the categories of ''mental processes'' or ''mathematical concepts'' for purposes of the Section 101 analysis. Charles Kim, Memorandum: Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, USPTO (August 4, 2025) (''August 4th Memorandum''). More specifically, the August 4th Memorandum instructs Examiners to ''determine that a claim recites a mental process when it contains limitation(s) that can practically be performed in the human mind' and reminds Examiners ''not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind." August 4th Memorandum, p. 2 (emphasis added). In fact, the August 4th Memorandum specifically references the published USPTO example 39, which is directed to ''training the neural network," as ''not recit[ing] a judicial exception ... [e]ven though 'training the neural network' involves a broad array of techniques and/or activities that may involve or rely upon mathematical concepts." August 4th Memorandum, p. 2 (emphasis added).
As each of the independent claims 1, 15, and 29 relate to the training of the machine learning model, and even more specifically recite the training of the machine learning model using training data derived from sample-specific image data related to a sample for a probe in a microarray, Applicant submits these claims are directed to patent-eligible subject matter as they cannot be practically performed in the human mind. As each of claims 2-3, 7-12, 16-17, 21-26, 30-31, 35-40, and 43 depend from one of claims 1, 15, and 29, Applicant submits that these claims are directed to patent-eligible subject matter at least due to their dependency from one of the above-referenced claims.
Examiner Response
Applicant argues that the claims do not recite a mental process because the claims include training a machine learning model, the rejection does not identify the machine learning training limitations as the judicial exception. Rather, the claims recite the mental process limitations of identifying an observed probe intensity value, and identifying at least one probe sequence or probe feature affecting a total probe intensity value. These limitations broadly recite observations and evaluation that can be practically performed in the human mind. Accordingly, the claims recite a mental process under Step 2A Prong One.
Applicant argues on pages 11-13
Applicant also respectfully submits that the Step 2A, Prong 2 analysis of the claims in the Office Action is improper, as it also fails to consider the recent updates to the MPEP and guidance provided in response to Ex Parte Desjardins.1 See Charles Kim, Memorandum: Advance notice of change to the MPEP in light of Ex Parte Desjardins, USPTO (December 5, 2025) (''December 5th Memorandum''). Specifically, the December 5th Memorandum amended MPEP § 2106.04(d), subsection III to state that:
In Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks.... In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, ... Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation ''adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task'' reflected the improvement disclosed in the specification.
Accordingly, the claims as a whole integrated what would otherwise be a
judicial exception instead into a practical application at Step 2A Prong Two, and there/ore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO) (emphasis added).
The December 5th Memorandum further amended MPEP § 2106.04(d)(l) to state that:
In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field.
Contrary to the above guidance, amendments to the MPEP, and precedential PTAB decision, there is no indication in the Office Action that the Examiner has referenced the specification to identify any such improvements. Applicant submits that the claims of the present application recite ''training ... a machine learning model'', similar to the claims in Ex Parte Desjardins. Additionally, the as-filed application expressly provides improvements identified from the claimed training of the machine learning model. For example, at least paragraphs [0067]-[0069] provide:
[0067] Though the total signal intensity R of a probe signal may be normalized in an attempt to improve the use or application of the total signal intensity R in genotyping applications, the normalization may rely on external reference datasets to compute an expected Norm R intensity value (Norm Rexpected), which may be less reliable for normalizing signals for some samples than for others ....
Disadvantages to using these external controls for performing such normalization may include difficulties in keeping external controls constant over time and/or across samples.
[0069] Embodiments are described herein for utilizing machine learning models to generate a predicted total signal intensity Rpredicted of a probe signal based on sample-specific image data. The predicted total signal intensity R w of a probe signal may be a total raw signal intensity or a normalized signal intensity. As the predicted total signal intensity Rpredicted of the probe signal is based on sample-specific image data, the predicted total signal intensity Rpredicted of the probe signal may be more accurate than an expected Norm R intensity value Norm Rpredicted that is based on external data. The predicted total signal intensity Rpredicted may be used in downstream genotyping applications to improve the accuracy of the application. Additionally, the use of a trained machine learning model to generate the predicted signal intensity Rpredicted may allow for more effective on-device processing at the genotyping device ... (emphasis added)
As indicated above, Applicant's specification expressly identifies how the claimed training of the machine learning model provides an improvement in both the functioning of a computer, and an improvement to other technology or a technical field (i.e., downstream genotyping applications).
As each of the independent claims 1, 15, and 29 include similar, but not identical, subject matter related to the training of the machine learning model, and the specification identifies how the claimed training of the machine learning model provides an improvement in both the functioning of a computer and other technology or a technical field (i.e., downstream genotyping applications), Applicant submits that the claims at least incorporate the alleged abstract idea into a practical application as highlighted by the above guidance, amendments to the MPEP, and precedential PTAB decision.
Accordingly, reconsideration and withdrawal of the rejection of claims 1-3, 7-12, 15-17, 21-26, 29-31, 35-40, 43 under 35 U.S.C. 101 are respectfully requested.
Examiner Response
Applicant argues that the claims integrate the recited mental process into a practical application by improving probe intensity prediction and downstream genotyping, the claims do not recite the particular features responsible for those asserted improvements. Rather, the claims broadly recite training a machine learning model to predict probe intensity without requiring the specific technological improvements described in the specification. Accordingly, the additional elements do not integrate the recited mental process into a practical application.
Regarding the 103 Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in view of the new grounds of rejection necessitated by the amendment.
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.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
Step 1: Determining if the claim falls within a statutory category.
Step 2A: Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and Step 2A is a two prong inquiry. MPEP 2106.04(II)(A). Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2104.04(a)(2). The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 2B: If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106).
Claims 1-3, 7-12, 15-17, 21-26, 29-31, 35-40, and 43 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-3, and 7-12 are directed to a method (a process), Claim 15-17, and 21-26 are directed to a computer-readable storage medium (a manufacture), and Claims 29-31, 35-40, and 43 are directed to a computing device comprising one or more processors (a machine). Therefore, Claims 1-3, 7-12, 15-17, 21-26, 29-31, 35-40, and 43 are directed to a process, machine or manufacture or composition of matter.
Regarding claim 1
Step 2A Prong 1
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “sample-specific image data “, “probe “, and “machine learning model”) [see MPEP 2106.04(a)(2)(III)].
“identifying an observed probe intensity value for the sample based on the sample-specific image data” (e.g., data identification by looking/observing image features)
“identifying at least one of a probe sequence or one or more probe features effecting a total probe intensity value for the sample” (e.g., analyzing information to determine/identify variables that affect an output, conceptual selection/association)
“wherein the training data is derived from the same sample specific image data as testing data that may be separated for testing the trained machine learning model” (e.g., merely deciding what data is to be used in training of a model, a human can decide preferred data samples for ma model to ingest)
Claim 1 further recites the following mathematical concepts, that in each case under the broadest reasonable interpretation, covers performance of mathematical relationships, mathematical formulas or equations, and mathematical calculations but for recitation of generic computer components (e.g., “sample-specific image data “, “probe “, and “machine learning model”) [see MPEP 2106.04(a)(2)(I)].
“determine a predicted probe intensity value based on an input of the at least one of the probe sequence or the one or more probe features” (e.g., establishing or applying a mathematical relationship between input variables and an output variable)
“wherein the predicted probe intensity value is a predicted total signal intensity of the signal associated with the sample for the probe” (e.g., model calculation of producing a numerical quantity/result)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “sample-specific image data “, “probe “, and “machine learning model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). The Examiner notes that this is used throughout the claim limitations, and is rejected thusly for each claim which recites the same language.
Regarding the “receiving sample-specific image data, wherein the sample-specific image data comprises a signal associated with a sample for a probe in a microarray relating to a single individual” this additional element is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of data gathering for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “training, using training data derived from the sample-specific image data comprising the signal associated with the sample for the probe in the microarray relating to the single individual, a machine learning model” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). The examiner notes that the limitation generally links the use of the judicial exception to a particular technological environment and data source without reciting a particular technological manner of training the model..
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “sample-specific image data “, “probe “, and “machine learning model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “receiving sample-specific image data, wherein the sample-specific image data comprises a signal associated with a sample for a probe in a microarray relating to a single individual” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, i.e., pre-solution activity of data gathering. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “training, using training data derived from the sample-specific image data comprising the signal associated with the sample for the probe in the microarray relating to the single individual, a machine learning model” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 2
Step 2A Prong 1
Claim 2 does not introduce any new abstract ideas, but recites the abstract idea identified in its parent claims.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the sample-specific image data comprises a raw x signal having a first intensity value of a first colored signal that represents a fluorescent label for a genotype A”, and “wherein the sample-specific image data comprises a raw y signal having a second intensity value of a second colored signal that represents a fluorescent label for a genotype B” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of choosing particular data, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the sample-specific image data comprises a raw x signal having a first intensity value of a first colored signal that represents a fluorescent label for a genotype A”, and “wherein the sample-specific image data comprises a raw y signal having a second intensity value of a second colored signal that represents a fluorescent label for a genotype B” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of pre-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 3
Step 2A Prong 1
Claim 3 does not introduce any new abstract ideas, but recites the abstract idea identified in its parent claims.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the observed probe intensity value is a raw probe intensity value or a normalized probe intensity value”, and “wherein the predicted probe intensity value is a predicted raw probe intensity value or a predicted normalized probe intensity value” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of choosing particular data, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the observed probe intensity value is a raw probe intensity value or a normalized probe intensity value”, and “wherein the predicted probe intensity value is a predicted raw probe intensity value or a predicted normalized probe intensity value” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of pre-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 7
Step 2A Prong 1
Claim 7 does not introduce any new abstract ideas, but recites the abstract idea identified in its parent claims.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the machine learning model is a linear regression model or a random forest model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). The examiner notes this is just defining what the model used to process the abstract idea comprises of.
Regarding the “wherein the input of the one or more probe features comprise k-mer features of the probe” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of choosing particular data, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the machine learning model is a linear regression model or a random forest model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Regarding the “wherein the input of the one or more probe features comprise k-mer features of the probe” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of pre-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 8
Step 2A Prong 1
Claim 8 does not introduce any new abstract ideas, but recites the abstract idea identified in its parent claims.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the machine learning model is a random forest model or a neural network” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). The examiner notes this is just defining what the model used to process the abstract idea comprises of.
Regarding the “wherein the input of the one or more probe features comprises an entire predefined set of probe features” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of choosing particular data, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the machine learning model is a random forest model or a neural network” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Regarding the “wherein the input of the one or more probe features comprises an entire predefined set of probe features” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of pre-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 9
Step 2A Prong 1
Claim 9 does not introduce any new abstract ideas, but recites the abstract idea identified in its parent claims.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the machine learning model is a neural network” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). The examiner notes this is just defining what the model used to process the abstract idea comprises of.
Regarding the “wherein the input comprises the probe sequence”, and “wherein the probe sequence is a 50bp probe sequence” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of choosing particular data, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the machine learning model is a neural network” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Regarding the “wherein the input comprises the probe sequence”, and “wherein the probe sequence is a 50bp probe sequence” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of pre-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 10
Step 2A Prong 1
Claim 10 does not introduce any new abstract ideas, but recites the abstract idea identified in its parent claims.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the neural network is a hybrid neural network comprising a convolutional portion and a fully-connected feed forward portion” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). The examiner notes this is just defining what the model used to process the abstract idea comprises of.
Regarding the “wherein the input comprises the probe sequence for the convolutional portion and the one or more probe features for the fully-connected feed forward portion” limitation, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of choosing particular data, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the neural network is a hybrid neural network comprising a convolutional portion and a fully-connected feed forward portion” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Regarding the “wherein the input comprises the probe sequence for the convolutional portion and the one or more probe features for the fully-connected feed forward portion” limitation, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of pre-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 11
Step 2A Prong 1
Claim 11 does not introduce any new abstract ideas, but recites the abstract idea identified in its parent claims.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the one or more probe features comprise at least one of a primer melting temperature (TM) under one or more salt concentrations or a GC content” limitation, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of choosing particular data, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the one or more probe features comprise at least one of a primer melting temperature (TM) under one or more salt concentrations or a GC content” limitation, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of pre-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 12
Step 2A Prong 1
Claim 12 does not introduce any new abstract ideas, but recites the abstract idea identified in its parent claims.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the sample- specific image data is received from a genotyping device” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data from a device, i.e., pre-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)). Regarding the “wherein the microarray comprises a BeadArray” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of choosing particular data, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)). Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the sample- specific image data is received from a genotyping device” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of pre-solution activity of data gathering. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “wherein the microarray comprises a BeadArray” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of pre-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claims 15-17, and 21-26
Claims 15-17, and 21-26 recites a computer-readable storage medium, which correspond directly to the method steps of 1-3, and 7-12. The addition of generic computer components executing instructions are insufficient to render the claims subject matter eligible for the same reasons as described above. Specifically:
Claim 15 corresponds to claim 1, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 1.
Claim 16 corresponds to claim 2, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 2.
Claim 17 corresponds to claim 3, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 3.
Claim 21 corresponds to claim 7, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 7.
Claim 22 corresponds to claim 8, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 8.
Claim 23 corresponds to claim 9, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 9.
Claim 24 corresponds to claim 10, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 10.
Claim 25 corresponds to claim 11, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 11.
Claim 26 corresponds to claim 12, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 12.
Regarding claims 29-31, and 35-40
Claims 29-31, and 35-40 recites a computer-readable storage medium, which correspond directly to the method steps of 1-3, and 7-12. The addition of generic computer components executing instructions are insufficient to render the claims subject matter eligible for the same reasons as described above. Specifically:
Claim 29 corresponds to claim 1, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 1.
Claim 30 corresponds to claim 2, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 2.
Claim 31 corresponds to claim 3, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 3.
Claim 35 corresponds to claim 7, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 7.
Claim 36 corresponds to claim 8, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 8.
Claim 37 corresponds to claim 9, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 9.
Claim 38 corresponds to claim 10, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 10.
Claim 39 corresponds to claim 11, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 11.
Claim 40 corresponds to claim 12, with the added recitation of generic computer components to execute instructions to perform the same abstract method steps od claim 12.
Regarding claim 43
Step 2A Prong 1
Claim 43 does not introduce any new abstract ideas, but recites the abstract idea identified in its parent claims.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the imaging system is a local imaging subsystem of a computing device that also comprises the at least one processor” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the imaging system is a local imaging subsystem of a computing device that also comprises the at least one processor” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
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.
Claim(s) 1-3, 11, 15-17, 25, 29-31, 39, and 43 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 20110029251 A1, referred to as Huang), in view of Li et al. ("A competitive hybridization model predicts probe signal intensity on high density DNA microarrays.", referred to as Li), in view of Baek et al. (“Segmentation and intensity estimation of microarray images using a gamma-t mixture model.”, referred to as Baek), in view of Piccolo, et al. ("A single-sample microarray normalization method to facilitate personalized-medicine workflows.", referred to as Piccolo).
Regarding claim 1, Huang teaches, a computer-implemented method comprising:
receiving sample-specific image data, wherein the sample-specific image data comprises a signal associated with a sample for a probe in a microarray relating to a single individual (Huang [0019]: Describes a microarray’s labeled-hybridization detection is performed by acquiring optical signals form the array (commonly via scanning/imaging). Where the data produced for a given sample’s array run is sample-specific signal data (images/signal data).;[0069]: Describes processing each individual sample on a SNP microarray and obtaining probe intensities (PM/MM) for that sample. A probe’s intensity is the measured signal associated with that sample for that probe on the microarray. Microarray probe ‘intensity’ is produced from scanner-acquired microarray images (spot pixel intensities), and that the cited reference collectively describes that workflow)
Although Huang teaches receiving sample-specific … data, wherein the sample-specific … data comprises a signal associated with a sample for a probe in a microarray relating to a single individual. It does not teach that the sample-specific data is image data.
(Huang teaches receiving, for each individual sample, microarray probe signal/intensity data (probe intensities0 associated with a sample for a probe in a microarray. Baek teaches that such probe signals are capture as microarray image data (spot images/pixel intensities) and used to derive the intensity value.)
Baek teaches sample-specific image data (Page 459-461 Section 2-2.3 and Fig. 1: Describes microarray image analysis in which an original microarray spot image (pixels with measured intensities, R/G channel pixel intensities) is processed via segmentation and intensity estimation to obtain the spot’s (probe’s) intensity signal. Corresponding to receiving sample image data where the image data comprises the signal for the probe spot corresponding to the sample);
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Huang’s microarray signal detection with Baek’s image-based microarray scanning. Doing so would have enabled the system to accurately segment images and pixel-based intensity estimation.
identifying an observed probe intensity value for the sample based on the sample-specific image data (Huang [0069]: Describes identifying , for each sample, probe intensities (PM/MM). These probe intensities are the observed intensity values corresponding to the probe signal for the sample.);
Although Huang in view of Baek teaches receiving sample-specific image data, wherein the sample-specific image data comprises a signal associated with a sample for a probe in a microarray relating to a single individual, and identifying an observed probe intensity value for the sample based on the sample-specific image data. They do not teach identifying at least one of a probe sequence or one or more probe features effecting a total probe intensity value for the sample
Li teaches, identifying at least one of a probe sequence or one or more probe features effecting a total probe intensity value for the sample (Pages 6585-6586, Introduction, and Page 6586-6587, A competitive hybridization model: Describes that differences in probe signal intensity are explained through sequence-specific thermodynamic properties, because the probe’s oligonucleotide sequence is the defining probe property, and provides a model where the observed signal intensity (SI) depends on a probe-specific factor derived from thermodynamic energy (ΔGd). Corresponding to identifying a probe sequence which effects a total probe intensity.);
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Huang’s identification of observed probe intensities of samples with Baek’s image-based microarray scanning, with Li’s probe sequence and probe-specific properties. Doing so would have enabled the system to more accurately model and interpret probe intensity values by considering known probe sequence-dependent thermodynamic effects.
Huang, in view of Baek, in view of Li teaches, training, using training data derived from the sample-specific image data(Huang [0072], and [0093-0095]: Describes training computational/algorithmic models using a training set comparing measured array intensity data (and genotype information), and states that training samples are used to “ to establish and tune the algorithm models” with separate test samples that do not overlap. These establish/tune using training samples to train a machine learning model using training data derived from the sample’s measured array signa/intensity data.)
Although Huang, in view of Baek, in view of Li teaches, training, using training data derived from the sample-specific image data, they do not teach comprising the signal associated with the sample for the probe in the microarray relating to the single individual.
Piccolo teaches comprising the signal associated with the sample for the probe in the microarray relating to the single individual(Pages 338-339, Section 2.1, and Page 343, Section 4.1: Describes processing each microarray individually using only information intrinsic to that array, wherein model parameters are estimated form probe data of the same array rather than external reference samples. It estimates model parameters using a subset of probes from the array and subsequently predicting probe intensity values for probes of that same array, where the model is trained using data derived from the same individual sample from testing/prediction data that are obtained.)
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the system of Huang, in view of Baek, in view of Li, with the sing sample model training of Piccolo. Doing so would have enabled the system to eliminate dependence on external reference samples while improving consistency and facilitate individualized microarray analysis.
Huang, in view of Baek, in view of Li teaches, in view of Piccolo teaches a machine learning model to determine a predicted probe intensity value based on an input of the at least one of the probe sequence or the one or more probe features, wherein the predicted probe intensity value is a predicted total signal intensity of the signal associated with the sample for the probe (Li Pages 6585-6586, Introduction, and Page 6586-6587, A competitive hybridization model: Describes determining a predicted probe signal intensity for an individual probe using a model in which probe-specific parameters are computed form sequence-based thermodynamics properties (e.g., ΔGd computed using a nearest-neighbor model, used to derive kd). It defines SI as the observed signal intensity for the probe (the probe’s signal intensity measured from the microarray experiment). This teaches determining a predicted probe intensity value based on an input corresponding to the probe’s sequence-derived features (thermodynamic properties), where the predicted value corresponds to a predicted total signal intensity of the signal associated with the sample for the probe.), and wherein the training data is derived from the same sample specific image data as testing data that may be separated for testing the trained machine learning model (Huang [0072]: Describes separating training samples form test samples for evaluating performance, where the training samples are used to “ to establish and tune the algorithm models” and the test samples do not overlap the training samples.; [0093]: Describes that the training set includes intensity information (probe intensity data), supporting that the training/testing data are derived from the same type of experiment-derived signal data.).
Regarding claim 2, Huang, in view of Baek, in view of Li, in view of Piccolo teaches, the computer-implemented method of claim 1.
Huang further teaches, wherein the sample-specific image data comprises a raw x signal having a first intensity value of a first colored signal that represents a fluorescent label for a genotype A, and wherein the sample-specific image data comprises a raw y signal having a second intensity value of a second colored signal that represents a fluorescent label for a genotype B ([0083-0085], Table 5: Describes that microarray genotyping uses two different fluorescent reporter dyes corresponding to different alleles (genotypes), disclosing that a first dye (VIC) measures the A allele and a second dye (FAM) measures the B allele. The measured dye intensities therefore correspond to two raw intensity signals (i.e., a first-channel intensity and a second-channel intensity) for the sample’s probe corresponding to a raw x signal and a raw y signal having first and second intensity values. These correspond to the sample-specific data including a first colored fluorescent-label intensity representing genotype A and a second colored fluorescent-label intensity representing genotype B.).
Regarding claim 3, Huang, in view of Baek, in view of Li, in view of Piccolo teaches, the computer-implemented method of claim 2.
Huang, in view of Baek, in view of Li, in view of Piccolo further teaches wherein the observed probe intensity value is a raw probe intensity value or a normalized probe intensity value (Huang [0069-0070]: Describes identifying observed probe intensity values in both a raw form (“raw probe intensities”). Which shows that the observed probe intensity value is a raw probe intensity value or a normalized probe intensity value.), and wherein the predicted probe intensity value is a predicted raw probe intensity value or a predicted normalized probe intensity value (Li Page 6585 Abstract and Introduction, and Page 6587 A competitive hybridization model: Describes predicting probe signal intensity for individual probes and explains probe-dependent intensity differences using sequence-specific thermodynamics, where the probe’s oligonucleotide sequence determines thermodynamic parameters that affect the measured signal intensity. Signal intensity (SI) is defined as observed probe signals. Determining a predicted probe intensity value based on probe sequence-derived features, where the predicted value corresponds to predicted signal intensity.).
Regarding claim 11, Huang, in view of Baek, in view of Li, in view of Piccolo teaches, the computer-implemented of claim 1.
Huang, in view of Baek, in view of Li, in view of Piccolo further teaches wherein the one or more probe features comprise at least one of a primer melting temperature (TM) under one or more salt concentrations or a GC content (Huang [0069-0070]: Describes that probe features include GC content and performs regression modeling using GC content as a variable affecting probe intensity.).
Regarding claims 15-17, and 25 which recites substantially the same limitations as claims 1-3, and 11 and further recites a computer-readable storage medium… executed by a processor(Huang [0062-0063]: Describes executing the methods by a computer with generic computer hardware which contains storage devices, processors and other storage mediums to execute those instructions.) to perform the method steps of claims 1-3, and 11, respectively and are rejected for the same reasons as described above.
Regarding claims 29-31, and 39 which recites substantially the same limitations as claims 1-3, and 11 and further recites a system(Huang [0062-0063]: Describes executing the methods by a computer system with generic computer hardware which contains storage devices, processors and other storage mediums to execute those instructions.) to perform the method steps of claims 1-3, and 11, respectively and are rejected for the same reasons as described above.
Regarding claim 43, Huang, in view of Baek, in view of Li, in view of Piccolo teaches, the system of claim 29.
Huang further teaches, wherein the imaging system is a local imaging subsystem of a computing device that also comprises the at least one processor ([0062-0063]: Describes a computer system that contains subsystems that execute instructions, store data and process the data on those systems with processors.).
Claim(s) 7, 8, 21, 22, 25, and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 20110029251 A1, referred to as Huang), in view of Li et al. ("A competitive hybridization model predicts probe signal intensity on high density DNA microarrays.", referred to as Li), in view of Baek et al. (“Segmentation and intensity estimation of microarray images using a gamma-t mixture model.”, referred to as Baek),in view of Piccolo, et al. ("A single-sample microarray normalization method to facilitate personalized-medicine workflows.", referred to as Piccolo), in view of Pelossof et al. (“Affinity regression predicts the recognition code of nucleic acid-binding proteins”, referred to as Pelossof).
Regarding claim 7, Huang, in view of Baek, in view of Li, in view of Piccolo teaches, the computer-implemented of claim 1.
Huang, further teaches, wherein the machine learning model is a linear regression model or a random forest model (Huang [0069-0070]: Describes using a linear regression modeling approach by performing regression on probe-related variables using training data.),
Although Huang, teaches wherein the machine learning model is a linear regression model or a random forest model. It does not teach wherein the input of the one or more probe features comprise k-mer features of the probe.
Pelossof teaches, wherein the input of the one or more probe features comprise k-mer features of the probe (Page 2-3 Results, and Page 16-17 Figure 1: Describes “Each TF pro-tein sequence is represented by its K-mer count features as a row in P, and each DNA probe sequence by its k-mer count features as a row in D”, and “Affinity regression decomposes the binding intensity for each TF and DNA probe as a weighted interaction between the k-mer features of the probe and the K-mer features of the TF amino acid sequence. Training the interaction model involves solving a regularized bilinear regression to minimize errors in reconstructing the probe intensity data across all TFs and probes.” Which represents that each DNA probe sequence using k-mer count features and training a regression model to reconstruct/predict probe-level binding intensities based on those k-mer features.).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Huang’s linear regression model with Pelossof’s k-mer probe features. Doing so would have enabled the system to account for sequence-dependent determinants of probe intensity using k-mer features.
Regarding claim 8, Huang, in view of Baek, in view of Li, in view of Piccolo teaches, the computer-implemented of claim 1.
Huang, in view of Baek, in view of Li, in view of Pelossf teaches, wherein the machine learning model is a random forest model or a neural network (Pelossf Page 8, Affinity regression gives accurate motifs for diverse homeodomains: Describes using a random forest model (via PreMoTF) trained to predict outputs from input features.), and wherein the input of the one or more probe features comprises an entire predefined set of probe features (Pelossf Page 3 Results: Describes representing each DNA probe by a fixed k-mer count features vector (a predefined feature set used as model input), including selecting k (k=6) for probe features.).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Huang’s linear regression model with Pelossof’s k-random forest model and predefined features. Doing so would have enabled the system to complex, potentially nonlinear relationships between probe sequence-derived features and probe intensity values.
Regarding claims 21, and 22 which recites substantially the same limitations as claims 7, and 8 and further recites a computer-readable storage medium… executed by a processor(Huang [0062-0063]: Describes executing the methods by a computer with generic computer hardware which contains storage devices, processors and other storage mediums to execute those instructions.) to perform the method steps of claims 7, and 8, respectively and are rejected for the same reasons as described above.
Regarding claims 35 and 36 which recites substantially the same limitations as claims 7, and 8 and further recites a system(Huang [0062-0063]: Describes executing the methods by a computer system with generic computer hardware which contains storage devices, processors and other storage mediums to execute those instructions.) to perform the method steps of claims 7, and 8, respectively and are rejected for the same reasons as described above.
Claim(s) 9, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 20110029251 A1, referred to as Huang), in view of Li et al. ("A competitive hybridization model predicts probe signal intensity on high density DNA microarrays.", referred to as Li), in view of Baek et al. (“Segmentation and intensity estimation of microarray images using a gamma-t mixture model.”, referred to as Baek), in view of Piccolo, et al. ("A single-sample microarray normalization method to facilitate personalized-medicine workflows.", referred to as Piccolo), in view of Alipanahi et al. (“Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning”, referred to as Alipanahi), in view of Illimina (“Infinium iSelect Custom Genotyping Assays”, referred to as Illimina, Inc.).
Regarding claim 9 Huang, in view of Baek, in view of Li, in view of Piccolo teaches, the computer-implemented of claim 1.
Although Huang teaches using a linear regression model, it does not teach using a neural network.
Alipanahi teaches wherein the machine learning model is a neural network (Page 836 DeepBind models identify deleterious genomic variants: Describes training a deep neural network machine learning model.)
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Huang’s linear regression model with Alipanahi’s neural network. Doing so would have enabled the system to better predict probe intensity values.
Huang, in view of Baek, in view of Li, in view of Piccolo, in view of Alipanahi teaches wherein the input comprises the probe sequence (Alipanahi 831 Results: Describes that DeepBind uses sequence input (a set of sequences) that is processed by a convolution stage scanning across the sequence, so that the model input comprises the probe sequence.)
Although Huang, in view of Baek, in view of Li, in view of Piccolo, in view of Alipanahi teaches wherein the machine learning model is a neural network, and wherein the input comprises the probe sequence. They do not teach wherein the probe sequence is a 50bp probe sequence.
Illumina, Inc. teaches, wherein the probe sequence is a 50bp probe sequence (Page 1, Bead Types and Assay Design: Describes “Infinium BeadChips use 50-mer probes carefully designed to hybridize selectively to a locus”, which uses 50mer 950bp) probes, to identify the probe sequences.).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Alipanahi’s probe sequence with Illumina, Inc.’s 50bp probe sequence. Doing so would have enabled the system to train on standard microarray probe designs for genotyping, to predict probe intensity values for genotyping assays.
Regarding claim 10, Huang, in view of Baek, in view of Li, in view of Piccolo, in view of Alipanahi, in view of Illumina, Inc. teaches, The computer-implemented method of claim 9.
Alipanahi further teaches, wherein the neural network is a hybrid neural network comprising a convolutional portion and a fully-connected feed forward portion, and wherein the input comprises the probe sequence for the convolutional portion and the one or more probe features for the fully-connected feed forward portion (Page 831-832 Introduction, and Results: Describes a hybrid neural network architecture comprising a convolutional stage that scans motif detectors across an input sequence and a subsequent nonlinear neural network stage that combines pooled motif responses using learned weights. The convolution stage operates on the input sequence, and the resulting pooled motif features are provided as inputs to the fully connected neural network portion to generate a binding score. Corresponding to a hybrid neural network comprising a convolutional portion and a fully-connected layer feed-forward portion, wherein the probe sequence is provided to the convolutional portion and probe-derived features are provided to the fully-connected portion.).
Regarding claims 23, and 24 which recites substantially the same limitations as claims 9, and 10 and further recites a computer-readable storage medium… executed by a processor(Huang [0062-0063]: Describes executing the methods by a computer with generic computer hardware which contains storage devices, processors and other storage mediums to execute those instructions.) to perform the method steps of claims 9, and 10, respectively and are rejected for the same reasons as described above.
Regarding claims 37 and 38 which recites substantially the same limitations as claims 9, and 10 and further recites a system(Huang [0062-0063]: Describes executing the methods by a computer system with generic computer hardware which contains storage devices, processors and other storage mediums to execute those instructions.) to perform the method steps of claims 9, and 10, respectively and are rejected for the same reasons as described above.
Claim(s) 12, 26, and 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 20110029251 A1, referred to as Huang), in view of Li et al. ("A competitive hybridization model predicts probe signal intensity on high density DNA microarrays.", referred to as Li), in view of Baek et al. (“Segmentation and intensity estimation of microarray images using a gamma-t mixture model.”, referred to as Baek), in view of Piccolo, et al. ("A single-sample microarray normalization method to facilitate personalized-medicine workflows.", referred to as Piccolo), in view of Illimina (“Infinium iSelect Custom Genotyping Assays”, referred to as Illimina, Inc.).
Regarding claim 12, Huang, in view of Baek, in view of Li, in view of Piccolo teaches, the computer-implemented method of claim 1.
Although Huang, in view of Baek, in view of Li, in view of Piccolo teaches, the computer-implemented method of claim 1. They do not teach wherein the sample- specific image data is received from a genotyping device, wherein the microarray comprises a BeadArray.
Illumina, Inc. teaches, wherein the sample-specific image data is received from a genotyping device, wherein the microarray comprises a BeadArray (Page 1 Introduction, and Bead Types and Assay Design: Describes that the Infinium assay performs array-based genotyping and that dual-color fluorescent staining is detected by a HiScan or iScan system (genotyping device. The Infinium assay uses BeadChip formats within its portfolio of BeadArray products, where the microarray comprises s BeadArray ).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Huang’s linear regression model with Illumina, Inc’s BeadArray genotyping platform. Doing so would have enabled the system to obtain sample-specific image/signal intensity data from a standard genotyping device.
Regarding claim 26 which recites substantially the same limitations as claim 12 and further recites a computer-readable storage medium… executed by a processor(Huang [0062-0063]: Describes executing the methods by a computer with generic computer hardware which contains storage devices, processors and other storage mediums to execute those instructions.) to perform the method steps of claim 12, respectively and are rejected for the same reasons as described above.
Regarding claim 40 which recites substantially the same limitations as claim 12 and further recites a system(Huang [0062-0063]: Describes executing the methods by a computer system with generic computer hardware which contains storage devices, processors and other storage mediums to execute those instructions.) to perform the method steps of claim 12, respectively and are rejected for the same reasons as described above.
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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/D.T.R./Examiner, Art Unit 2128
/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
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