DETAILED ACTION
Applicant’s response filed 5/4/2026 has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied.
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 .
Status of the Claims
Claims 15-33 are pending and under consideration in this action. Claims 1-14 were previously canceled.
Priority
The instant application is 371 of PCT/US2020/052112, filed 9/23/2020, which claims priority to U.S. Provisional Application number 62/904,220, filed 9/23/2019 and U.S. Provisional Application number 62/937,064, filed 11/19/2019, as reflected in the filing receipt mailed 11/21/2022. The claim for domestic benefit for claims 15-33 is acknowledged. As such, the effective filing date of claims 15-33 is 9/23/2019.
Drawings
The objections to the drawings are withdrawn in view of Applicant’s amendments to the Specification filed 5/4/2026 (Applicant’s Remarks, Pg. 13).
Specification
The objections to the abstract and Specification are withdrawn in view of Applicant’s amendments to the abstract filed 5/4/2026 (Applicant’s Remarks, Pg. 13).
Claim Objections
The objections to claims 15, 30, and 32 are withdrawn in view of Applicant’s amendments to the claims filed 5/4/2026 (Applicant’s Remarks, Pg. 13-14).
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 15-33 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
This rejection is newly recited and necessitated by claim amendment.
Claim 15 recites the limitation “by a computing device comprising at least one processor in communication with , the method comprising” in lines 2-3 of the claim. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. It appears the limitation is missing what the processor is in communication with; for example, at least one memory device, as recited in Specification Para. [0011] and analogous to claim 32. This rejection can be overcome by amendment of claim 15 to include the device the processor is in communication with. Claims 16-29 are also rejected due to their dependence from claim 15.
Claims 15, 30, and 32 recite the limitation “execute/executing the trained machine-learning model using the received patient-specific data as inputs and receiving an output of a likelihood of a disease diagnoses for the at least one patient”. There is insufficient antecedent basis for the trained machine-learning model in the claim, since there is no prior mention of this phrase earlier in the claim. The claim recites the training of a tree-based classifier, but there are other aspects of the model (see lines 5-16 of claim 15 and corresponding “building a model relating the elements of the unprocessed raw data” in claims 30 and 32) This rejection can be overcome by amendment of claims 15, 30, and 32 to recite “execute/executing the trained ” or clarity that the model is a machine-learning-based model earlier in the claim. Claims 16-29, 31, and 33 are also rejected due to their dependence from claims 15, 30, and 32.
Claim Rejections - 35 USC § 101
Maintained Rejections
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 15-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Any newly recited portion is necessitated by claim amendment.
Step 1:
In the instant application, claims 15-29 are directed towards a method, claims 30-31 are directed towards a manufacture, and claims 32-33 are directed towards a machine, which falls into one of the categories of statutory subject matter (Step 1: YES).
Step 2A, Prong One:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claims 15, 30, and 32 recite a mathematical concept in “building a model relating elements of the unprocessed raw data”; a mental process (i.e., an evaluation of the disease spectrum in view of categories) in “partitioning a human disease spectrum into one or more categories”; a mathematical concept (i.e., the HMMs described in Para. [0009]/[0085]) in “constructing a set of statistical models representing the one or more categories”; a mathematical concept (see specification Para. [0087] for example formula) in “determining, for each of the one or more categories, a sequence likelihood defect (SLD) value”; a mathematical concept in “training a tree-based classifier based on one or more features extracted from the unprocessed raw data”; a mathematical concept in “assigning a weight to each of the one or more features based at least in part on the SLD values”; a mathematical concept in “constructing an estimator based on the statistical model and the weighted one or more features”; and a mathematical concept (i.e., using cross-validation as described in Para. [0063] of the specification) in “validating the estimator”.
Claims 16 and 33 recite a mental process (i.e., an evaluation of the likelihood to determine intervention) in “generating/generate one or more intervention possibilities based on the predicted likelihood”.
Claim 18 recites a mental process (i.e., an observation of the contents of the raw data) in “wherein the unprocessed raw data consists essentially of records of diagnostic codes generated during past medical encounters of the plurality of patients”.
Claim 19 recites a mathematical concept in “wherein the set of statistical models are further constructed to represent genders, a treatment cohort, and a control cohort based on the unprocessed raw data”.
Claim 20 recites a mental process (i.e., an evaluation of the disease diagnosis) in “wherein the disease diagnosis is related to Autism Spectrum Diagnosis (ASD) and is at least one of the following: Angelman Syndrome, Fragile X Syndrome, Landau-Kleffner Syndrome, Prader-Willi Syndrome, Tardive Dyskinesia, and Williams Syndrome”.
Claim 21 recites a mental process (i.e., an observation of the contents of the raw data) in “wherein the unprocessed raw data includes diagnostic history of at least some of the plurality of patients”.
Claim 22 recites a mental process (i.e., an evaluation of the likelihood prediction) in “wherein the likelihood is predicted for different cohorts of the plurality of patients at different time-points”.
Claim 23 recites a mental process (i.e., an evaluation of the likelihood prediction) in “wherein the likelihood provides one or more cues to other disorders misdiagnosed as a different disorder for the at least one patient”.
Claim 24 recites a mental process (i.e., an observation of the contents of the raw data) in “wherein the unprocessed raw data includes one or more individual diagnostic codes from prior doctor visits made by one or more of the plurality of patients”.
Claim 25 recites a mental process (i.e., an observation of the patient-specific data) in “wherein the patient-specific data includes one or more sequences of diagnostic codes from past doctor's visits by the at least one patient”.
Claim 26 recites a mental process (i.e., an evaluation of how the likelihood was predicted) in “wherein the likelihood is predicted without any new blood work for the at least one patient”.
Claim 27 recites a mathematical concept (see creation of time series in Para. [0083] of the specification) in “wherein the model further comprises a representation of each patient of the plurality of patients by a mapped trinary series to infer one or more population-level models”.
Claim 28 recites a mental process (i.e., an evaluation of the trinary series) in “wherein each of the mapped trinary series is stratified by gender, disease-category, and disease diagnosis status”.
Claim 29 recites a mental process (i.e., an evaluation of how the model was run) in “wherein each of the inferred population-level models includes a modeling of treatment and control for each gender in each disease category separately”.
Claim 31 recites a mathematical concept (see creation of time series in Para. [0083] of the specification) and a mental process (i.e., an evaluation of the trinary series and how the model was run) in “wherein the model further comprises a representation of each patient of the plurality of patients by a mapped trinary series to infer one or more population-level models, each of the mapped trinary series is stratified by gender, disease-category, and disease diagnosis status, and each of the inferred population-level models includes a modeling of treatment and control for each gender in each disease category separately”.
These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis is Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)), and organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships.
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Specifically, claims 15, 30, and 32 involve nothing more than building a model, partitioning a disease spectrum into categories, constructing statistical models, determining a sequence likelihood defect value, training a tree-based classifier, assigning weights, constructing an estimator, and validating the estimator. The steps reciting building a model, constructing statistical models, determine a sequence likelihood defect value, training a tree-based classifier, assigning weights, constructing an estimator, and validating the estimator are, under the BRI, performed using mathematical operations. The Specification (see Para. [0009] and [0085]) discloses that hidden Markov models may be used as statistical models. The Specification (see Para. [0087]) also discloses the formula used to calculate the sequence likelihood defect. The Specification (see Para. [0089]-[0090]) also discloses that the risk estimator includes one or more semi-supervised or supervised learning modules, and the parameters and associated model structures of the pipeline may be transformed by the patient specific data to a set of engineered features, and the feature vectors realized on the treatment and control sets may be then used to train a gradient-boosting classifier. The classifier is tuned to the tuned to determine the optimal combination of weights. The Specification (see Para. [0063]) discloses that cross-validation is performed for the estimator.
Additionally, since there are no specifics in the methodology, partitioning a disease spectrum into categories, is something that under BRI, one could perform mentally. Therefore, the claimed steps are not further defined beyond something that reads on performing a calculation using a computer as a tool, and merely looking at data and making a determination. As such, said steps are directed to judicial exceptions. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
Step 2A, Prong Two:
In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP § 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP § 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP § 2106.04(d)(III)). The following independent claims recite limitations that equate to additional elements:
Claim 15 recites “a computing device comprising at least one processor”; “retrieving unprocessed raw data associated with a plurality of patients”; “generating one or more categorical time series based on the unprocessed raw data”; “receiving patient-specific data associated with at least one patient”; and “executing the trained machine-learning model using the received patient-specific data as inputs and receiving an output of a likelihood of a disease diagnosis for the at least one patient”.
Claim 30 recites “a non-transitory computer-readable medium comprising instructions executed by a processor”; “retrieving unprocessed raw data associated with a plurality of patients”; “generating one or more categorical time series based on the unprocessed raw data”; “receiving patient-specific data associated with at least one patient”; and “executing the trained machine-learning model using the received patient-specific data as inputs and receiving an output of a likelihood of a disease diagnosis for the at least one patient”.
Claim 32 recites “the apparatus comprising at least one processor in communication with at least one memory device”; “retrieve unprocessed raw data associated with a plurality of patients”; “generate one or more categorical time series based on the unprocessed raw data”; “receive patient-specific data associated with at least one patient”; and “execute the trained machine-learning model using the received patient-specific data as inputs and receiving an output of a likelihood of a disease diagnosis for the at least one patient”.
Regarding the above cited limitations in claims 15, 30, and 32 of (i) retrieving/retrieve unprocessed raw data associated with a plurality of patients (claims 15, 30, and 32); (ii) generating/generate one or more categorical time series based on the unprocessed raw data (claims 15, 30, and 32); and (iii) receiving/receive patient-specific data associated with at least one patient (claims 15, 30, and 32). These limitations equate to insignificant, extra-solution activity of mere data gathering because these limitations gather data before or after the recited judicial exceptions of building a model, constructing statistical models, determine a sequence likelihood defect value, training a tree-based classifier, assigning weights, constructing an estimator, and validating the estimator (see MPEP § 2106.04(d)).
Regarding the above cited limitations in claims 15, 30, and 32 of (iv) a computing device comprising at least one processor (claim 15); (v) a non-transitory computer-readable medium comprising instructions executed by a processor (claim 30); and (vi) the apparatus comprising at least one processor in communication with at least one memory device (claim 32). These limitations require only a generic computer component, which does not improve computer technology. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
Regarding the above cited limitation in claims 15, 30, and 32 of (vii) execute the trained machine-learning model using the received patient-specific data as inputs and receiving an output of a likelihood of a disease diagnosis for the at least one patient. This limitation equates to an extra-solution “apply it” step, because the limitation is used to physically execute the trained model without providing any details of how the output likelihood of a disease diagnosis is predicted for any disease (see MPEP § 2106.05(f)).
Additionally, none of the recited dependent claims recite additional elements which would integrate the judicial exception into a practical application. Specifically, claim 17 recites a data gathering step analogous to claims 15, 30, and 32 above. As such, claims 15-33 are directed to an abstract idea (Step 2A, Prong Two: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The instant independent claims recite the same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitations in claims 15, 30, and 32 of (i) retrieving/retrieve unprocessed raw data associated with a plurality of patients (claims 15, 30, and 32); and (iii) receiving/receive patient-specific data associated with at least one patient (claims 15, 30, and 32). These limitations do not include any specific steps for acquiring raw data for patients, or for acquiring patient specific data. Under the BRI, these limitations are merely receiving data for the subsequent steps of building the model using the raw data, constructing statistical models, and executing the trained model. Therefore, these limitations equate to receiving/transmitting data over a network, which the courts have established as a WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Regarding the above cited limitations in claims 15, 30, and 32 of (iv) a computing device comprising at least one processor (claim 15); (v) a non-transitory computer-readable medium comprising instructions executed by a processor (claim 30); and (vi) the apparatus comprising at least one processor in communication with at least one memory device (claim 32). These limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept (see MPEP § 2106.05(d) and MPEP § 2106.05(f)).
Regarding the above cited limitation in claims 15, 30, and 32 of (ii) generating/generate one or more categorical time series based on the unprocessed raw data. This limitation is considered to be insignificant extra-solution activity of mere data gathering. This step is incidental to the primary process of building a model, constructing statistical models, determine a sequence likelihood defect value, training a tree-based classifier, assigning weights, constructing and validating the estimator, wherein generated time series data are merely inputs for the model. This is analogous to performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) (see MPEP § 2106.05(g)).
Regarding the above cited limitation in claims 15, 30, and 32 of (vii) execute the trained machine-learning model using the received patient-specific data as inputs and receiving an output of a likelihood of a disease diagnosis for the at least one patient. This limitation when viewed individually and in combination, is a well-understood, routine and conventional (WURC) limitations as taught by Vaughn et al. (U.S. Patent Application Publication 2017/0069216; cited in the IDS dated 3/23/2022; previously cited). Vaughn et al. discloses a method for diagnosing or identifying a subject as at risk for having a developmental disorder using a trained machine learning model using input data specific to an individual (Para. [0011], [0090], and [0093]).
These additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 15-33 are not patent eligible.
Response to Arguments under 35 U.S.C. 101
Applicant’s arguments filed 5/4/2026 have been fully considered but they are not persuasive.
1. Applicant argues that in view of the recent PTAB decision reversing an examiner's final rejection under 35 U.S.C. § 101 of claims directed to artificial intelligence (AI)-based business methods, Ex Parte Carmody, Appeal 2025-002843, (PTAB Dec. 30, 2025), the following framework is presented to reflect the most up-to-date guidance from the USPTO. While the panel in Carmody found that the claims at issue recited an abstract idea at Alice Step 2A, Prong One, it held that they recited additional elements that integrated the judicial exception into a practical application at Alice Step 2A, Prong Two, thus finding the claims patent-eligible under§ 101. The PTAB stated that "the claims recite additional elements that integrate the judicial exception into a practical application, and the claims are patent-eligible under § 101" (Applicant’s Remarks, Pg. 14-15).
It is respectfully submitted that this is not persuasive for the following reasons:
The instant application is different from the AI-based methods recited in Carmody. In Carmody, the limitation of “train at least one of the plurality of modular plug-and-play tactic-specific models using machine learning with a second training dataset comprising labeled feature vectors, wherein each of the labeled feature vectors comprises set of orchestration features labeled with an indication of whether or not an engagement resulted within a defined time period” provides the improvement in the training of the models under Step 2A, Prong Two. The improvement is recited in the Specification, as the Specification recites “[t]his modular approach to tactic recommendation (i.e., with a separate model for each tactic) enables the model for each tactic to be updated and improved separately and independently from other tactic-specific models, and also enables models for new tactics to be easily incorporated into tactic recommendation model 475 (e.g., as a plug-and-play module)”.
In the instant case, the only limitation reciting training of the model is “training a tree-based classifier based on one or more features extracted from the unprocessed raw data”. The Specification (see at least Para. [0009], [0012], [0041], [0050], [0053], [0090], and [0110]) discloses the use of a tree-based classifier to train the model and that collected data is used for training a predictive classifier/pipeline. However, the Specification does not appear to recite a specific improvement in the model based on the tree-based classifier. In fact, the Specification (see Para. [0110]) discloses that “at least one of a plurality of ML methods and algorithms may be applied, which may include but are not limited to: linear or logistic regression, instance-based algorithms, regularization algorithms, decision trees, Bayesian networks, cluster analysis, association rule learning, artificial neural networks, deep learning, dimensionality reduction, and support vector machines”, indicating that a variety of algorithms can be applied to train the model, and any improvement is not limited to a tree-based classifier. Therefore, even if this training limitation did not recite a mathematical concept, it does not provide an improvement in the model training. This argument is thus not persuasive.
2. Applicant argues that in Ex Parte Desjardins et al Rehearing Decision, the claimed invention of Application No. 16/319,040 is related to training machine learning models. More specifically, as supported by the description in the Specification, the invention relates to an improvement in efficiency of machine-learning models through training. In Desjardins, the PTAB pointed out that an improvement to training is an improvement to an Artificial Intelligence. The PTAB states that the following limitation of independent Claim 1 reflects the improvement: "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." The PTAB was persuaded that the above limitation constitutes an improvement to how the machine learning model itself operates, and not, for example, an identified mathematical calculation (Applicant’s Remarks, Pg. 15-17).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in argument (1) above, the only limitation reciting training of the model is “training a tree-based classifier based on one or more features extracted from the unprocessed raw data”. The instant Specification does not appear to provide any specific improvements arising from the tree-based classifier, instead reciting that a variety of algorithms can be applied in model training (see at least Para. [0110]). This is different than the limitations recited in Desjardins, where the specific training method “learns new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems” (see December 5, 2025, USPTO Memo, Pg. 2). This argument is thus not persuasive.
3. Applicant argues that the USPTO, on December 5, 2025, issued an advance notice of change to the Manual of Patent Examining Procedure (MPEP) to incorporate Desjardins, a decision addressing subject matter eligibility under § 101. More specifically, at page 1, the memorandum states "Examiners are expected to consider existing precedent like Enfish, as discussed in MPEP 2106, in addition to these updates when assessing eligibility under 35 USC 101, particularly when evaluating claims related to machine learning or artificial intelligence." Furthermore, at page 2, the memorandum discusses an amendment to MPEP 2106.04(d) and states, "The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification." Here, the claimed invention offers advances over prior art NGS methodologies, i.e., the claimed invention addresses genotyping "genes that include large repeat structures in their nucleic acid code [that] have proven difficult to genotype, due to inherent limitations of the NGS technology" (Applicant’s Remarks, Pg. 17-18).
It is respectfully submitted that this is not persuasive for the following reasons:
The December 5, 2025 Memo (see Pg. 2) further states improvements in the training of the model: “Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. 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”. As described in arguments (1) and (2) above, the instant claims do not appear to reflect specific improvements in the training of a tree-based classifier based on the Specification.
MPEP 2106.05(a) recites:
After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification. However, the claim itself does not need to explicitly recite the improvement described in the specification (e.g., thereby increasing the bandwidth of the channel"). The full scope of the claim under the BRI should be considered to determine if the claim reflects an improvement in technology (e.g., the improvement described in the specification). In making this determination, it is critical that examiners look at the claim "as a whole," in other words, the claim should be evaluated "as an ordered combination, without ignoring the requirements of the individual steps." When performing this evaluation, examiners should be "careful to avoid oversimplifying the claims" by looking at them generally and failing to account for the specific requirements of the claims. McRO, 837 F.3d at 1313, 120 USPQ2d at 1100.
Additionally, the alleged improvements indicated by Applicant are not commensurate in scope with the claimed invention. Applicant appears to assert that the claimed features provide an improvement in the analysis of genes that have proven difficult to genotype due to large repeat structures in their nucleic acid code (Applicant’s Remarks, Pg. 18). However, amended claims 15, 30, and 32 do not provide any indication of processing genes, specifically reciting “retrieving unprocessed raw data associated with a plurality of patients” and “receiving patient-specific data associated with at least one patient”. Under the BRI, the amended claims could process any type of patient raw data, not only gene data. Therefore, it appears the alleged improvements are not commensurate in scope with the claimed invention. This argument is thus not persuasive.
4. Applicant also argues that claimed invention is similar to Desjardins. In the Instant application as filed, as well as amended Claim 1, there is clearly described an extremely specific process of training and using a machine-learning model that is very similar to that of Desjardins. Instant Claim 1 describes building a model relating elements of the unprocessed raw data, wherein building the model further comprises: a) partitioning a human disease spectrum into one or more categories; b) generating one or more categorical time series based on the unprocessed raw data; c) constructing a set of statistical models representing the one or more categories; d) determining, for each of the one or more categories, a sequence likelihood defect (SLD) value; e) training a tree-based classifier based on one or more features extracted from the unprocessed raw data; f) assigning a weight to each of the one or more features based at least in part on the SLD values; g) constructing an estimator based on the statistical models and the weighted one or more features; and h) validating the estimator. That is, the description shows how specific training steps is used to improve the speed and accuracy of diagnosis of multiple disorders to provide for earlier intervention. Accordingly, the subject matter the technical field, the claimed subject matter, and the specific technological improvement in the Instant application and Desjardins are demonstrated to be similar (Applicant’s Remarks, Pg. 18-19).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in arguments (1) and (2) above, the instant claims do not appear to reflect specific improvements in the training of a tree-based classifier based on the Specification. This is different than the improvement recited in Desjardins, wherein claim limitations reflect the improvement recited in the Specification in “training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems” (see December 5, 2025 Memo, Pg. 2). This argument is thus not persuasive.
5. Applicant also argues that the claimed invention is similar to Enfish. Similar to the technical improvements claimed in Enfish, in the instant claim recitations is disclosed a process of specifically training and using the model to improve the speed and accuracy of diagnosis of multiple disorders to provide for earlier intervention. Thus, the clear technical improvements increased efficiency and accuracy of the machine-learning (ML) model is described in Claim 1, like in Enfish. Thus, the i) overall technical field of software inventions and ii) claimed technological improvements in the present application and claims are both very similar to Enfish. Accordingly, under at least the updated guidance and the decisions rendered in Desjardins and Enfish, Applicant respectfully submits that the present claims are not directed to merely mental processes or mathematical concepts, but instead to a specific, computer-implemented technological process that trains various machine-learning models in a specific manner to provide for improve efficiency and accuracy, with technical improvements very similar to those in Desjardins and Enfish (Applicant’s Remarks, Pg. 19-20).
It is respectfully submitted that this is not persuasive for the following reasons:
In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. It was the specification’s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. The claim was not simply the addition of general purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts (see MPEP § 2106.05(a)(I)).
As described in the arguments above, the instant claims to do not appear to recite an improvement in the training of the tree-based classifier based on the Specification. The instant Specification (see Para. [0110]) recites numerous algorithms that can be implemented in the model to estimate the risk of disease diagnosis. Additionally, unlike Enfish, the instant claims or Specification do not appear to improve the way the computer stores or retrieves data in memory (see Specification Para. [0074] and [0105], reciting that any personal computer or workstation can be used to carry out the steps of the instant claims). Therefore, the instant claims do not appear to recite improvements in computer functionality analogous to Enfish and this argument is not persuasive.
6. Applicant argues that with respect to Step 2A, Prong One, the Examiner alleges that certain claim steps are mental processes (Office Action, p. 5), but Applicant submits that these steps cannot practically be performed mentally. In Claim 1, the Examiner identified "predicting a likelihood of a disease diagnosis for the least one patient using the model based upon the received patient-specific data" as a mental process. However, performing analysis of patient-specific data and comparing to a myriad of different disorders involves processing massive amounts of data representing millions to billions of data points that cannot be mentally processed, compared, or analyzed by a human. The steps in Claim 1 cannot be practically be performed mentally due to the massive scale of disorders and data and the computational requirements involved (Applicant’s Remarks, Pg. 20-21).
It is respectfully submitted that this is not persuasive for the following reasons:
The limitation referenced by Applicant of “predicting a likelihood of a disease diagnosis for the least one patient using the model based upon the received patient-specific data” has been removed from amended claims 15, 30, and 32. As amended, claims 15, 30, and 32 still recites a mental process in the following limitation, as described in Step 2A, Prong One above: “partitioning a human disease spectrum into one or more categories”. Since there are no specifics in the claim that indicate steps or parameters for partitioning the human disease into one or more categories, this is a step that can reasonably be performed in the human mind. Accordingly, amended claims 15, 30, and 32 recite abstract ideas, and this argument is not persuasive.
7. Applicant argues that the Examiner also alleges that these claims cover mathematical concepts. Office Action, p. 5. However, the claims do not explicitly recite any mathematical concepts, formulas, or algorithms. At best, the claims recite limitations that may be based on or involve mathematical concepts, but no mathematical concepts are recited in the claims themselves. Applicant points the Examiner to the recent August 4 memo from Charles Kim, reminding Examiners that a limitation that merely involves math is not an abstract idea. Accordingly, the claims do not recite a judicial exception under Prong One of the Step 2A analysis. For this reason alone, the claims satisfy the requirements of the subject matter eligibility test and Applicant respectfully requests that the Examiner withdraw the rejection (Applicant’s Remarks, Pg. 21).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in Step 2A, Prong One above, the following limitations in amended claims 15, 30, and 32 recite mathematical concepts: “building a model relating elements of the unprocessed raw data”; “constructing a set of statistical models representing the one or more categories”; “determining, for each of the one or more categories, a sequence likelihood defect (SLD) value”; “training a tree-based classifier based on one or more features extracted from the unprocessed raw data”; “assigning a weight to each of the one or more features based at least in part on the SLD values”; “constructing an estimator based on the statistical model and the weighted one or more features”; and “validating the estimator”.
MPEP § 2106.04(a)(2)(I)(C) recites:
"A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation."
Applicant’s Specification (see Para. [0009] and [0085]) discloses that hidden Markov models may be used as statistical models. The Specification (see Para. [0087]) also discloses the formula used to calculate the sequence likelihood defect. The Specification (see Para. [0089]-[0090]) also discloses that the risk estimator includes one or more semi-supervised or supervised learning modules, and the parameters and associated model structures of the pipeline may be transformed by the patient specific data to a set of engineered features, and the feature vectors realized on the treatment and control sets may be then used to train a gradient-boosting classifier. The classifier is tuned to the tuned to determine the optimal combination of weights. The Specification (see Para. [0063]) discloses that cross-validation is performed for the estimator.
Therefore, when given its broadest reasonable interpretation in light of the Specification, the limitations of “building a model relating elements of the unprocessed raw data”; “constructing a set of statistical models representing the one or more categories”; “determining, for each of the one or more categories, a sequence likelihood defect (SLD) value”; “training a tree-based classifier based on one or more features extracted from the unprocessed raw data”; “assigning a weight to each of the one or more features based at least in part on the SLD values”; “constructing an estimator based on the statistical model and the weighted one or more features”; and “validating the estimator” equate to mathematical calculations and relationships. This argument is thus not persuasive.
8. Applicant argues that under Step 2A, Prong Two, because the instant claims do not recite a judicial exception, no further analysis is necessary. However, assuming, arguendo, that Applicant's claims recite a judicial exception, the claims include additional limitations that integrate the alleged exception into a practical application. "One way to demonstrate such integration is when the claimed invention ... improves the functioning of a computer, or to any other technology or technical field." MPEP 2106.04(d)(l), emphasis added. As described above in Desjardins, the PTAB found the subject matter claimed in Desjardins to identify improvements in training the machine learning model itself, which thus, "integrates an abstract idea into a practical application." Similarly, the present claims describe improvements to specific training steps is used to improve the speed and accuracy of diagnosis of multiple disorders to provide for earlier intervention. Thus, the clear technical improvements increased efficiency and accuracy of the machine-learning (ML) model is described in Claim 1, like in Enfish (Applicant’s Remarks, Pg. 21-22).
It is respectfully submitted that this is not persuasive for the following reasons:
MPEP 2106.04(d)(II) recites:
The analysis under Step 2A Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon (including products of nature). Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).
The limitations for building and training the model, as indicated by Applicant, of “partitioning a human disease spectrum into one or more categories”; “constructing a set of statistical models representing the one or more categories”; “determining, for each of the one or more categories, a sequence likelihood defect (SLD) value”; “training a tree-based classifier based on one or more features extracted from the unprocessed raw data”; “assigning a weight to each of the one or more features based at least in part on the SLD values”; “constructing an estimator based on the statistical model and the weighted one or more features”; and “validating the estimator” have been identified as judicial exceptions in Step 2A, Prong One above. The integration of a judicial exception into a practical application can only be achieved by additional elements, not by a limitations that recites a judicial exception. Thus, the recited limitations are not considered as improvements in the speed and accuracy of diagnosis of multiple disorders to provide for earlier intervention; or as an improvement in efficiency and accuracy of the machine-learning model. This argument is thus not persuasive.
9. Applicant argues that under Step 2B, it is not necessary to consider Step 2B of the Alice/Mayo framework because the claims are eligible under either prong of Step 2A. Nevertheless, Applicant submits that the claims contain an inventive concept that amounts to significantly more than any alleged judicial exception. Furthermore, the analysis of Step 2B should include determining whether the claim recites additional elements that amount to significantly more than the judicial exception. Examiners should first identify whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluate those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)) (Applicant’s Remarks, Pg. 22-23).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in Step 2A, Prong Two and the arguments above, amended claims 15, 30, and 32 recite abstract ideas. Analysis under Step 2B shows that the following additional elements are WURC limitation of a generic computer, instructions to implement an abstract idea on a generic computing environment, insignificant extra-solution activity of mere data gathering analogous to In re Grams, or WURC limitations as taught by Vaughn et al.: “retrieving unprocessed raw data associated with a plurality of patients”; “generating one or more categorical time series based on the unprocessed raw data”; “receiving patient-specific data associated with at least one patient”; and “executing the trained machine-learning model using the received patient-specific data as inputs and receiving an output of a likelihood of a disease diagnosis for the at least one patient”. Therefore, when viewed individually and in combination, the instant claims do not recite significantly more than the judicial exception, and this argument is thus not persuasive.
Conclusion
No claims allowed.
Claims 15-33 appear to be free of the prior art for the same reasons as disclosed in the Conclusion of the Office action dated 11/13/2025.
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.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIANA P SANFORD whose telephone number is (571)272-6504. The examiner can normally be reached Mon-Fri 8am-5pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571)272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/D.P.S./Examiner, Art Unit 1687
/Lori A. Clow/Primary Examiner, Art Unit 1687
Prosecution Insights