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 ("RCE"), 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 5, 2025, has been entered.
Status of Claims
Claims 69-88 were previously pending and subject to a Final Office Action having a notification date of September 5, 2025 (“previous Final Office Action”). Following the previous Final Office Action, Applicant filed the RCE and an amendment on December 5, 2025 (“Amendment”), amending claims 69, 77, and 83.
The present Final Office Action addresses pending claims 69-88 in the Amendment.
The Examiner notes that most of the additions and deletions to claims 69, 73, and 83 in the Amendment were already entered in the previous amendment dated August 5, 2025.
Response to Arguments
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
Starting on page 9 of the Amendment, Applicant asserts that the present claims are not directed to "mental processes" because they require specific technological components and computational components that cannot be performed mentally. The Examiner disagrees that the present claims do not recite "mental processes."
The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). MPEP 2106.05(III).
Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019). MPEP 2106.05(III)(A).
However, claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer"). MPEP 2106.05(III)(C).
In the present case, the independent claims recite a mental process because a medical professional (e.g., laboratory geneticist) could practically in their mind analyze biological expression data of a current patient with that of known healthy and diseased patients to determine a disease status and development of the current patient, analyze/review parameters (e.g., metabolite levels) of a disease profile associated with the corresponding disease (e.g., comparing the disease profile to a metabolite profile of the current patient to determine scores), determine disease progression based on the analysis (e.g., based on scoring trends over time), and display a report with treatment recommendations based on the disease progression and patient classification. These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
While the Examiner agrees that a person cannot practically in their mind perform biometric data sequencing algorithms, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (e.g., determining level of biomarker in blood, Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 79 (2012)) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)) as all uses of the abstract idea (i.e., analyzing expression data) require sequencing user data to obtain the expression data.
While the Examiner also agrees that a person cannot practically in their mind execute multi-layer neural network computations with pooled data transmission between layers (in response to Applicant's assertion on page 9 of the Amendment) and that such NN execution is more than mere conventional computer-implementation (in response to Applicant's assertion on page 10 of the Amendment), the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). For instance, as NNs are typically configured to sequentially process input data through various hidden layers to generate an output (whereby results are pooled at each subsequent hidden layer and whereby the number of layers typically increases with increasing data complexity), then such limitation does not recite any specific details regarding how the NN is implemented such that these additional limitations provide only a result-oriented solution. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
In response to Applicant's assertion on pages 10-11 of the Amendment that the present NN architecture with its multi-layer pooled processing represents a technological advancement in diagnostic methodology, the Examiner asserts that using existing machine learning technology to perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved does not confer patent-eligibility. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 15.
Applicant's assertions on page 11 of the Amendment regarding the non-mental performability of the sequencing algorithm and the neural network architecture have already been addressed above.
Regarding Applicant's assertions on pages 11-12 of the Amendment that the present claims satisfy the machine or transformation test established in In re Bilski, the Examiner asserts that such assertion is moot because a claim that fails the Alice/Mayo test (like the present claims as set forth in the rejection below) is still ineligible even if it passes the machine or transformation test. DDR Holdings, LLC v. Hotels.com L.P., 773 F.3d 1245 (Fed. Cir. 2014). MPEP 2106.05(c).
Accordingly, the claims continue to be rejected under 35 USC 101.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §103
On pages 12-13 of the Amendment, Applicant takes the position that Luca, Apte, and Young do not teach or suggest the newly added limitations including analyzing the expression data via a first layer of the NN component, the first layer being a hidden layer of the NN component; performing further analysis of the NN component for each subsequent layer of the number of layers within the NN component, such that each subsequent layer receives transmitted processed data from a previous layer that is pooled with processed data from each of the subsequent layers, as presently claimed. The Examiner disagrees because Young teaches ([0027]-[0028] and Figure 2) that it was known in the machine learning art to configure a number of hidden layers of an ANN component based on a complexity of data being input to and used by the ANN to generate outputs (e.g., medical diagnostics per [0035]). As shown in Figure 2, there are a number of hidden layers, whereby data is analyzed by a first hidden layer and then further analyses are performed for each subsequent layer of the number of layers within the NN component, such that each subsequent layer receives transmitted processed data from a previous layer that is pooled with processed data from each of the subsequent layers. This arrangement advantageously improves the fit of customized input thereby resulting in more accurate predictive outputs ([0018]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the NN component of the Luca/Apte combination to include a number of hidden layers based on a complexity of the expression data whereby data is analyzed by a first hidden layer and then further analyses are performed for each subsequent layer of the number of layers within the NN component, such that each subsequent layer receives transmitted processed data from a previous layer that is pooled with processed data from each of the subsequent layers as taught by Young to advantageously improve the fit of customized input thereby resulting in more accurate predictive outputs. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 69-88 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more:
Subject Matter Eligibility Criteria - Step 1:
Claims 69-76 are directed to a method (i.e., a process), claims 77-82 are directed to a non-transitory computer-readable storage medium (i.e., a manufacture), and claims 83-88 are directed to a device (i.e., a machine). Accordingly, claims 69-88 are all within at least one of the four statutory categories. 35 USC §101.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 and July 2024 updates issued by the USPTO as incorporated into the MPEP, as supported by relevant case law), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 83 includes limitations that recite at least one abstract idea. Specifically, independent claim 83 recites:
A device comprising:
a processor configured to:
receive user data comprising biometric data corresponding to a category of biological traits of a user;
generate expression data based on the user data, the expression data generation comprising analyzing the user data via a sequencing algorithm, and determining the expression data as corresponding to at least a portion of the biological traits;
analyze, via a machine learning (ML) model, the expression data, the ML model comprising a neural network (NN) component, the NN component comprising a number of layers based on a complexity of the expression data, the analysis comprising:
analyzing the expression data via a first layer of the NN, the first layer being a hidden layer of the NN;
performing further analysis of the NN for each subsequent layer of the number of layers within the NN, such that each subsequent layer receives transmitted processed data from a previous layer that is pooled with processed data from each of the subsequent layers;
determine, based on the NN analysis, a current classification for the user, the classification corresponding to a current status of a disease and development of the disease as per the biological traits of the user;
analyze, by the device, via the ML model, a profile associated with the disease based on the classification, the ML analysis of the disease profile comprising analyzing parameters of the disease as identified within the disease profile that correspond to the classification of the user;
determine, based on the ML analysis of the disease profile, a progression of the disease; and
communicate, for display, an electronic report, the electronic report comprising a recommendation for treatment of the disease based on the determined progression of the disease and the current classification of the user.
The Examiner submits that the foregoing underlined limitations constitute “mental processes” because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). As an example, a medical professional (e.g., laboratory geneticist) could practically in their mind analyze biological expression data of a current patient with that of known healthy and diseased patients to determine a disease status and development of the current patient, analyze/review parameters (e.g., metabolite levels) of a disease profile associated with the corresponding disease (e.g., comparing the disease profile to a metabolite profile of the current patient to determine scores), determine disease progression based on the analysis (e.g., based on scoring trends over time), and display a report with treatment recommendations based on the disease progression and patient classification. These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
The Examiner submits that the foregoing underlined limitations constitute “certain methods of organizing human activity” because they relates to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). These limitations are similar to a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). MPEP 2106.04(a)(2)(II)(C).
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 70, 78, and 84 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) because they merely call for generating a “visualization related to the classification” which, at such high level of generality, could be performed in the human mind with pen and paper.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A device comprising:
a processor configured to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)):
receive user data comprising biometric data corresponding to a category of biological traits of a user;
generate expression data based on the user data, the expression data generation comprising analyzing the user data via a sequencing algorithm, and determining the expression data as corresponding to at least a portion of the biological traits (extra-solution activity (data gathering; e.g., determining level of biomarker in blood) as noted below, see MPEP § 2106.05(g));
analyze, via a machine learning (ML) model, the expression data, the ML model comprising a neural network (NN) component, the NN component comprising a number of layers based on a complexity of the expression data (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)), the analysis comprising:
analyzing the expression data via a first layer of the NN, the first layer being a hidden layer of the NN; (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f))
performing further analysis of the NN for each subsequent layer of the number of layers within the NN, such that each subsequent layer receives transmitted processed data from a previous layer that is pooled with processed data from each of the subsequent layers (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f));
determine, based on the NN analysis, a current classification for the user, the classification corresponding to a current status of a disease and development of the disease as per the biological traits of the user;
analyze, by the device, via the ML model, a profile of the disease based on the classification, the ML analysis of the disease profile comprising analyzing parameters of the disease as identified within the disease profile that correspond to the classification of the user;
determine, based on the ML analysis of the disease profile, a progression of the disease; and
communicate, for display, an electronic report, the electronic (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) report comprising a recommendation for treatment of the disease based on the determined progression of the disease and the current classification of the user.
For the following reasons, the Examiner submits that the above-identified additional limitations, when considered as a whole with the limitations reciting the at least one abstract idea, do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the user device and processor and the report being electronic and communicated, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitation of generating expression data based on the user data, via analyzing the user data with a sequencing algorithm and determining the expression data as corresponding to at least a portion of the biological traits, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (e.g., determining level of biomarker in blood, Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 79 (2012)) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)).
Regarding the additional limitations of how the expression data is analyzed using an NN component having a number of layers based on a complexity of the expression data to generate the current classification, where the analysis includes analyzing the expression data by a first hidden layer of the NN component and then performing further analysis whereby each subsequent layer receives transmitted processed data from a previous data that is pooled with processed data from each subsequent layer, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). For instance, as NNs are typically configured to sequentially process input data through various hidden layers to generate an output (whereby results are pooled at each subsequent hidden layer and whereby the number of layers typically increases with increasing data complexity), then such limitation does not recite any specific details regarding how the NN is implemented such that these additional limitations provide only a result-oriented solution.
Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
Similarly, the limitations reciting how the disease profile analysis is performed "via the ML model" provides no details regarding how the ML model performs the disease profile analysis such that it amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)).
Claims drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves those results, are almost always found to be ineligible for patenting under Section 101.” Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1356 (Fed. Cir. 2024).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Furthermore, looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 83 and analogous independent claims 69 and 77 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 83 and analogous independent claims 69 and 77 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 71, 79, and 85: These claims recite how the electronic report includes functionality to display recommended treatment disease status updates which just amounts to using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Claims 72-74, 80, and 86: These claims generically recite how the ML is an ensemble whereby the output of one model is an input to another model and thus amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). For instance, what type of ML model is each of the models in the ensemble? How are the ML models trained and executed? Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
Claims 75, 81, and 87: These claims recite how a type of the ML model corresponds to a type of the user biological traits which just amounts to reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
Claims 76, 82, and 88: These claims recite how the sequencing algorithm includes RNA sequency technology and thus do no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
When the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 83 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations of the user device and processor and the report being electronic, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of how the expression data is analyzed using an NN having a number of layers based on a complexity of the expression data to generate the current classification, where the analysis includes analyzing the expression data by a first hidden layer of the NN and then performing further analysis whereby each subsequent layer receives transmitted processed data from a previous data that is pooled with processed data from each subsequent layer, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). For instance, as NNs are typically configured to sequentially process input data through various hidden layers to generate an output (whereby results are pooled at each subsequent hidden layer and whereby the number of layers typically increases with increasing data complexity), then such limitation does not recite any specific details regarding how the NN is implemented such that these additional limitations provide only a result-oriented solution.
Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
Similarly, the limitations reciting how the disease profile analysis is performed "via the ML model" provides no details regarding how the ML model performs the disease profile analysis such that it amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)).
Claims drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves those results, are almost always found to be ineligible for patenting under Section 101.” Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1356 (Fed. Cir. 2024).
Regarding the additional limitations directed to generating expression data based on the user data, via analyzing the user data with a sequencing algorithm and determining the expression data as corresponding to at least a portion of the biological traits which the Examiner submits merely adds insignificant extra-solution activity to the abstract idea (see MPEP § 2106.05(g)), the Examiner has reevaluated such limitations and determined such limitations to not be unconventional as they merely consist of determining the level of a biomarker in blood by any means (Mayo, 566 U.S. at 79, 101 USPQ2d at 1968) and analyzing DNA to provide sequence information or detect allelic variants (Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016)).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Claims 71, 79, and 85: These claims recite how the electronic report includes functionality to display recommended treatment disease status updates which just amounts to using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Claims 72-74, 80, and 86: These claims generically recite how the ML is an ensemble whereby the output of one model is an input to another model and thus amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). For instance, what type of ML model is each of the models in the ensemble? How are the ML models trained and executed? Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
Claims 75, 81, and 87: These claims recite how a type of the ML model corresponds to a type of the user biological traits which just amounts to reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
Claims 76, 82, and 88: These claims recite how the sequencing algorithm includes RNA sequency technology and thus do no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
Therefore, claims 69-88 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 69, 70, 72, 74, 76-78, 82-84, and 88 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2020/0263255 to Luca et al (“Luca”) in view of U.S. Patent App. Pub. No. 2019/0211378 to Apte et al. (“Apte”) and U.S. Patent App. Pub. No. 2019/0171928 to Young ("Young"):
Regarding claim 69, Luca discloses a method comprising:
receiving, by a device, user data comprising biometric data corresponding to a category of biological traits of a user ([0081], [0349], and claims 108 and 116 disclose a computer apparatus for obtaining biological sample data (biometric data) of a patient such as urine, saliva, etc., where such biological/biometric data corresponds to some category of biological traits of the user (e.g., such as prostate/organ system, reproductive system, current conditions, etc.);
generating, by the device, expression data based on the user data ([0081] discloses determine gene expression levels in the sample (user data)), the expression data generation comprising analyzing the user data via a sequencing algorithm ([0141], [0310]-[0311] and [0327] disclose performing RNA sequencing on the user sample), and determining the expression data as corresponding to at least a portion of the biological traits (as the sample already corresponds to the biological traits as noted above, then the expression data generated from the sample also corresponds to the biological traits);
analyzing, by the device, via a machine learning (ML) model, the expression data, the ML model comprising a neural network (NN) component, the NN component comprising a number of layers ([0175]-[0182] discuss analyzing the expression data of the unknown sample using ML algorithms/models including NNs which necessarily include components (e.g., computer code portions) having layers),…, the analysis comprising:
analyzing the expression data via a first layer of the NN component ([0175]-[0182] discuss analyzing the expression data of the unknown sample using ML algorithms/models including NNs which would necessarily include analyzing the expression data by one of the layers of the NN component), …;
…
determining, by the device, based on the NN analysis, a current classification associated with the user ([0102]-[0103] and [0175]-[0182] discuss assigning patient expression profile to individual group contributing most to overall expression profile (determining “current classification”), where each group can be assigned a DESNT or non-DESNT status), the classification corresponding to a current status of a disease and development of the disease as per the biological traits of the user ([0100]-[0102] discusses how increasing pi values correspond to worsening outcomes for the cancers (current status of disease/development); the disease status/development is per the biological traits of the user because the DESNT/NON-DESNT status is determined based on the expression data which is determined based on the sample which corresponds to some category of biological traits of the user as noted above);
analyzing, by the device, via the ML model, a profile associated with the disease based on the classification, the ML analysis of the disease profile comprising analyzing parameters of the disease as identified within the disease profile that correspond to the classification of the user (as noted above, [0102] and [0180] discuss using the ML model to assign/classify the patient expression profile to the pi value group (e.g., such as a DESTN status group) having the greatest contribution to the overall expression profile based on the pi values; furthermore, [0101] discusses analyzing Gleason/PSA scores/tumor stage (parameters of disease profile) to determine cancer progression and [0102] discusses predicting cancer progression based on contribution of pi value of DESNT process to overall expression profile (analyzing parameters of disease profile corresponding to the classification of the user));
determining, by the device, based on the ML analysis of the disease profile, a progression of the disease ([0101] discusses determining cancer prognosis based on Gleason score/PSA score/tumor stage (i.e., based on the analysis of the disease parameters in the disease profile corresponding to the classification of the user) and [0102] discusses predicting cancer progression based on contribution of pi value of DESNT process to overall expression profile (determining disease progression based on ML analysis of the disease profile)); and
… recommendation for treatment of the disease based on the determined progression of the disease and the current classification of the user ([0308] and [0343] discuss determining appropriate treatment based on the DESNT status of the cancer (based on disease progression and current classification of the user)).
However, Luca might be silent regarding communicating, by the device, for display, an electronic report, the electronic report comprising the treatment recommendation.
Nevertheless, Apte teaches ([0255] and Figures 2 and 6) that it was known in the healthcare informatics art to display on a computing device therapy provision notifications (electronic report with treatment recommendations) generated by a therapy model based on analyzing a patient’s sequence reads ([0050]-[0051]) to advantageously communicate the suggested treatment to the patient and/or caretakers thereby improving patient health outcomes.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have communicated the electronic report for display by the device to advantageously communicate the suggested treatment to the patient and/or caretakers thereby improving patient health outcomes. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Furthermore, Luca appears to be silent regarding the NN including a number of layers based on a complexity of the expression data, the first layer being a hidden layer, and the analysis further including performing further analysis of the NN component for each subsequent layer of the number of layers within the NN component, such that each subsequent layer receives transmitted processed data from a previous layer that is pooled with processed data from each of the subsequent layers.
Nevertheless, Young teaches ([0027]-[0028] and Figure 2) that it was known in the machine learning art to configure a number of hidden layers of an ANN (where the ANN necessarily includes components (e.g., computer code portions) that represent/include the layers) based on a complexity of data being input to and used by the ANN to generate outputs (e.g., medical diagnostics per [0035]). As shown in Figure 2, there are a number of hidden layers, whereby data is analyzed by a first hidden layer and then further analyses are performed for each subsequent layer of the number of layers within the NN, such that each subsequent layer receives transmitted processed data from a previous layer that is pooled with processed data from each of the subsequent layers. This arrangement advantageously improves the fit of customized input thereby resulting in more accurate predictive outputs ([0018]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the NN component of the Luca/Apte combination to include a number of hidden layers based on a complexity of the expression data whereby data is analyzed by a first hidden layer and then further analyses are performed for each subsequent layer of the number of layers within the NN component, such that each subsequent layer receives transmitted processed data from a previous layer that is pooled with processed data from each of the subsequent layers as taught by Young to advantageously improve the fit of customized input thereby resulting in more accurate predictive outputs. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Regarding claim 70, the Luca/Apte/Young combination discloses the method of claim 69, further including generating a visualization related to the classification, wherein the electronic report comprises the visualization ([0254]-[0255] of Apte discloses how the displayed notification/electronic report includes an indication of the patient’s health issue characterization (classification); furthermore, as shown in Figures 2 and 6 of Apte, the notification/report includes various “visualization” (e.g., bar charts, tables, characters, etc.); similar to as discussed above, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have communicated the electronic report for display by the device to advantageously communicate the suggested treatment to the patient and/or caretakers thereby improving patient health outcomes. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Regarding claim 72, the Luca/Apte/Young combination discloses the method of claim 69, further including wherein the ML model is an ensemble ML model ([0177]-[0182] of Luca disclose ensemble learning).
Regarding claim 74, the Luca/Apte/Young combination discloses the method of claim 72, further including wherein the ensemble ML model comprises at least three ML models ([0175] and [0177] of Luca disclose an RF approach that constructs a plurality of decision trees (ML models) such as 10001 (at least three) trees/ML models ([0436] of Luca).
Regarding claim 76, the Luca/Apte/Young combination discloses the method of claim 69, further including wherein the sequencing algorithm comprises RNA sequencing technology ([0141] and [0327] of Luca)
Regarding claim 77, Luca discloses a non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by a processor, perform a method (Figure 4 illustrates a computing device 100 including memory 116 and processor 114 that executes instructions stored by the memory 116 per [0303]-[0305]).
The remaining limitations of claim 77 are disclosed by the Luca/Apte/Young combination as discussed above in relation to claim 69.
Claims 78 and 82 are disclosed by the Luca/Apte/Young combination as discussed above in relation to claims 70 and 76, respectively.
Regarding claim 83, Luca discloses a device (computing device 100 in Figure 4) comprising: a processor (processor 114).
The remaining limitations of claim 83 are disclosed by the Luca/Apte/Young combination as discussed above in relation to claim 69.
Claims 84 and 88 are disclosed by the Luca/Apte/Young combination as discussed above in relation to claims 70 and 76, respectively.
Claims 71, 79, and 85 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2020/0263255 to Luca et al (“Luca”) in view of U.S. Patent App. Pub. No. 2019/0211378 to Apte et al. (“Apte”) and U.S. Patent App. Pub. No. 2019/0171928 to Young ("Young"), and further in view of U.S. Patent App. Pub. No. 2015/0112710 to Haber et al. (“Haber”):
Regarding claim 71, the Luca/Apte/Young combination discloses the method of claim 69, further including monitoring a patient’s response to therapy ([0333] of Luca and [0256] of Apte).
However, the Luca/Apte/Young combination might be silent specifically regarding wherein the electronic report comprises functionality to display updates to a status of the disease based on the recommended treatment.
Nevertheless, Haber teaches ([0020], [0028 ]-[0029]) that it was known in the healthcare informatics art to utilize a predictive model to analyze patient data to generate likelihoods regarding adverse health outcomes, identify interventions/treatments for the predicted adverse health outcomes ([0101]), and present a comparison of outcome likelihoods across all possible clinical conditions of interest on a GUI ([0195]). The presented comparison on the GUI gives clinicians near real-time, patient specific risk and contextual information and improves over time with additional data such that the patient can be monitored over time as treatment progresses. For instance, [0063] discusses how dynamic risk can be affected by care received in the hospital and can change due, for example, to changes in conditions of the patient while in the hospital via the medical care that the patient receives while in the hospital as reflected in the patient's condition which is characterized by dynamic patient data that is updated with some frequency during the patient's hospital stay. This arrangement advantageously provides clinicians with sufficient contextual information and preventative action decision-making support to facilitate effective intervention ([0019]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the electronic report to include functionality to display updates to a status of the disease based on the recommended treatment in the system of the Luca/Apte/Young combination as taught by Haber to advantageously provides clinicians with sufficient contextual information and preventative action decision-making support to facilitate effective intervention. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claims 79 and 85 are rejected in view of the Luca/Apte/Young/Haber combination as discussed above in relation to claim 71.
Claims 73, 80, and 86 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2020/0263255 to Luca et al (“Luca”) in view of U.S. Patent App. Pub. No. 2019/0211378 to Apte et al. (“Apte”) and U.S. Patent App. Pub. No. 2019/0171928 to Young ("Young"), and further in view of Int’l Pub. No. WO 2017/201540 to Cahoon et al. (“Cahoon”):
Regarding claim 73, the Luca/Apte/Young combination discloses the method of claim 72, further including wherein various types of ML approaches can be used such as RF, NNs, SVMs, etc. ([0175] of Luca).
However, the Luca/Apte/Young combination might be silent regarding wherein a first ML model in the ensemble ML model provides an output, wherein the output is used as input for a next ML model within the ensemble model.
Nevertheless, Cahoon teaches ([0188]) that it was known in the machine learning and healthcare informatics art to chain output from one machine learning model into another machine learning model (for use in predicting cancer/disease diagnoses per [0084]) to improve efficiency and usage by avoiding movement of data between different locations and setting up of different processing environments to perform different processes on the data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for a first ML model in the ensemble ML model to provide an output used as input for a next ML model within the ensemble model in the system of the Luca/Apte/Young combination as taught by Cahoon to advantageously improve efficiency and usage by avoiding movement of data between different locations and setting up of different processing environments to perform different processes on the data. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claims 80 and 86 are rejected in view of the Luca/Apte/Young/Cahoon combination as discussed above in relation to claims 72 and 73.
Claims 75, 81, and 87 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2020/0263255 to Luca et al (“Luca”) in view of U.S. Patent App. Pub. No. 2019/0211378 to Apte et al. (“Apte”) and U.S. Patent App. Pub. No. 2019/0171928 to Young ("Young"), and further in view of U.S. Patent App. Pub. No. 2015/0006456 to Sudharsan (“Sudharsan”):
Regarding claim 75, the Luca/Apte/Young combination discloses the method of claim 69 but appears to be silent regarding wherein the ML model is a type of ML model that corresponds to a type of the biological traits within the user data, wherein the analysis via the ML model of the user data comprises selecting a certain type of ML model based on identification of the type of the biological traits.
Nevertheless, Sudharsan teaches that it was known in the healthcare informatics art to receive patient data ([0064]), extract metadata from the received patient data ([0070]) such as in relation to the patient’s weight, heigh, medical history, behavior (biological traits)([0074]), and select one or more models from a library of models (which are ML models per [0034]-[0040] and Figure 2) based on the extracted metadata (based on the biological traits) to predict medical occurrences ([0079]-[0080]). This arrangement advantageously utilizes ML models for predicting patient medical conditions that are tailored to the type of received patient data thereby increasing the accuracy of the determined predictions.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the ML model to be of a type of ML model that corresponds to a type of the biological traits within the user data, wherein the analysis via the ML model of the user data includes selecting a certain type of ML model based on identification of the type of the biological traits in the system of the Luca/Apte/Young combination as taught by Sudharsan to advantageously utilize an ML model for predicting patient medical conditions that is tailored to the type of received user data thereby increasing the accuracy of the determined predictions and because a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). The courts have made clear that the teaching, suggestion, or motivation test is flexible and an explicit suggestion to combine the prior art is not necessary. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007).
Claims 81 and 87 are rejected in view of the Luca/Apte/Young/Sudharsan combination as discussed above in relation to claim 75.
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686