DETAILED ACTION
This office action is based on the claim set filed on 02/18/2026.
Claims 1, and 19-20 have been amended.
Claim 8 has been canceled.
Claims 1-7 and 9-20 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/18/2026 has been entered.
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.
Claim 1-7 and 9-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-7 and 9-18 are drawn to a system, Claim 19 is drawn to an art of manufacturer, and Claim 20 is directed to a method and each of which is within the four statutory categories (i.e., a machine and a process). Claims 1-7 and 9-20 are further directed to an abstract idea on the grounds set out in detail below.
Under Step 2A, Prong 1, the steps of the claim for the invention represents an abstract idea of a series of steps that recite a process for modeling and predicting a future disease. Generating a model to predict a future risk of infectious disease based on the model performance are steps that could have been performed by a human mind but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for which both the instant claims and the abstract idea are defined as Metal Process that can be performed using human mind with the aid of pencil and paper.
Independent Claim 1 recites the steps of:
“a system comprising one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors perform operations comprising:
selecting, from a plurality of machine learning models configured to predict a future risk of infectious diseases by location, one or more ensembles of machine learning models according to one or more weighting schemes;
generating one or more scheme combination objects according to a validation period, wherein each scheme combination object of the one or more scheme combination objects comprise one or more of the selected one or more ensembles of machine learning models, each machine learning model in the one or more of the selected one or more ensembles of machine learning models being assigned a weight according to a corresponding weighting scheme and wherein the corresponding weighting scheme is applied by generating a weighted aggregation of model outputs over the validation period
selecting, for a time stamp in a test period, a scheme combination object by (i) evaluating a performance of the one or more scheme combination objects based on comparing one or more performance metrics of each of the one or more scheme combination objects across multiple ensemble levels, wherein the multiple ensemble levels comprise horizon level, location level, and location plus horizon level during the test period, and wherein the one or more performance metrics comprise prediction accuracy, precision, recall, or area under a receiver operating characteristic curve, and (ii) selecting the scheme combination object that achieves a highest relative performance score at an ensemble level;
generating a prediction of future risk of infectious disease for the time stamp based on the selected scheme combination object by applying the weighted aggregation of model outputs to input data corresponding to the time stamp; and
initiating the performance of one or more prediction-based actions based on the prediction”.
Independent Claims 19 and 20 recite similar steps as in Claim 1.
These limitations, as drafted, given the broadest reasonable interpretation cover performance of the limitations by a human mind with aid of pen and paper reciting an abstract idea for Mental Process along with Mathematical Calculations and relationships that constitute Mathematical Concepts but for the recitation of generic computer components. For example, the limitations encompass a user to select and generate a model using weight schemes for a plurality of features for predicting disease risk, execute different combination of schemes during a period of time and select the best prediction model providing results and perform an action to the predictive results, which are steps that that could have been performed by a human to implement the abstract idea and are steps reciting mental process that could have been performed using a human mind with aid of pen and paper and mathematical concepts, but other than the mere nominal recitation of "processor, memory, machine learning model", to implement the abstract idea for performing the steps of observing, evaluating, judgment and opinion which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III). Accordingly, the claim limitations (in BOLD) recite an abstract idea. Any limitations not identified above as part of the Mental Process are deemed "additional elements," and will be discussed in further detail below.
Under Step 2A, Prong 2, this judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas, linking the abstract idea to a particular technological environment. In particular, the claims recite the additional elements such as “processor, memory, machine learning model, non-transitory computer readable recording medium” that iteratively takes input data and analyzes said data to determine an output to performing generic computer functions for predicting a future disease such that it amounts no more than adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f), generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d). For example, applying a ready trained machine learning model recited in the claims at a high level of generality and is described in the specification in an arbitrary form without disclosing a specific algorithm using available data for allowing the model to learn patterns and relationships within the data and implement it to perform the claimed function. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 "merely include[ing] instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application. Accordingly, looking at the claim as a whole, individually and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Under step 2B, the claims do not include additional elements that are sufficient to amount to "significantly more" than the judicial exception because as mentioned above, the additional elements amount to no more than generic computing components, recited at a high level of generality, do not present improvements to another technology or technical field, nor do they affect an improvement to the functioning of the computer itself, that amount to no more than mere instruction to perform the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f). There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, See Alice, 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention."). The claims are not patent eligible.
Dependent Claims 2-7 and 9-18 include all of the limitations of claim(s) 1, and therefore likewise incorporate the above-described abstract idea. While the depending claims add additional limitations, such as
As for claims 3-4, 11, and 16, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper, reciting an abstract idea for Mental Process along with mathematical calculations and relationships that constitute Mathematical Concepts but for the recitation of generic computer components. The claims recite additional elements “processor, machine learning model(s)” that implement the identified abstract idea. These hardware components are recited at a high level of generality to perform the steps that amounts to no more than the words "apply it" with a computer because it appears to intend to do so, which would still amount to mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Additionally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements amount to more than mere instruction to apply the exception using generic computer component and have been re-evaluated under the “significantly more” analysis. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claims 2, 5, 9, 15, and 17, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper, reciting an abstract idea for Mental Process but for the recitation of generic computer components. The claims recite additional elements “processor, machine learning model, user interface, display device” that implement the identified abstract idea. These hardware components are recited at a high level of generality to perform the steps, e.g., rendering graphical representation, that amounts to no more than the words "apply it" with a computer because it appears to intend to do so, which would still amount to mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Additionally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements amount to more than mere instruction to apply the exception using generic computer component and have been re-evaluated under the “significantly more” analysis. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claims 6-7, 10, 12-14, and 18, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper but for the recitation with a computing system, or generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
Claims Free of Prior Art
Claims 1-7 and 9-20 have been found by the examiner to be free of prior art. A thorough search of the prior art was conducted and the examiner could not find a single reference or combination of references with adequate rationale to combine that would teach the claimed invention.
Regarding independent claim 1, and similarly claims 19 and 20, none of the prior art teach or fairly suggests the limitation of “selecting, for a time stamp in a test period, a scheme combination object by (i) evaluating a performance of the one or more scheme combination objects based on comparing one or more performance metrics of each of the one or more scheme combination objects across multiple ensemble levels, wherein the multiple ensemble levels comprise horizon level, location level, and location plus horizon level during the test period, and wherein the one or more performance metrics comprise prediction accuracy, precision, recall, or area under a receiver operating characteristic curve”. The closest prior art of record is/are:
- Pirozzi et al. (US 11,922,284 Bl) teaches a real-time ensemble unit generating ensemble of machine learning models according to one or more weighting schemes and feature data comprises data objects associated with a time stamp and location and select an output of a particular temporally trained machine learning model deemed most accurate and/or most reliable as a cross-model output with lowest error to predict future values/parameters for measuring the effect of the control polices, prevention, and cost.
- Achin et al. (US 2023/0051833 Al) teaches modeling infectious disease(s) to predict occurrence of the infectious disease during different time periods in the future where an exploration engine selects the modeling procedures with suitability scores above a threshold suitability score and selects the modeling procedures may execute different scenarios and evaluate their suitability scores within a specified range of the highest suitability score.
- Garg et al. (US 2022/0076848 Al) teaches determine error as performance metric using root mean squared errors (RMSEs) for the noted models, mean absolute percentage errors (MAPES) for the noted models, mean absolute errors (MAEs) for the noted models, mean percentage errors (MPEs) for the noted models.
- Ruple et al. (US 2023/0154623 A1) discloses predictive disease identification via simulations improved using machine learning wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types.
- Zhang et al. (US 2021/0089937 A1) discloses a mathematical formulation for model validation and model selection in presence of test data feedback evaluating accuracy performance of schemes.
However, no prior art was found teaching individually, or suggesting in combination, all of the features of the applicants' invention, as disclosed in the claimed invention.
Response to Amendment
Applicant's arguments filed 02/18/2026 have been fully considered by the Examiner and addressed as the following:
In the remarks, Applicant argues in substance that:
Applicant's arguments with respect to the 35 U.S.C. § 101 rejection on page 11-13.
On page 12-13 of the remarks, Applicant argues “As shown in paragraph [0026] of the original specification of the present application, the judicial exception ... is integrated into the practical application (e.g., comparing the parameters and the risk indexes to generate a risk information), and each of the amended claims 1, 6, 11 analyzed as a whole is significantly more (generate a determining result according to the parameters, the trusting levels, and determine whether to adjust a medical decision according to the determining result) than the abstract idea implemented using the central processing unit... the present disclosure provides a verifiable technical foundation that substantially improves system reliability and enables more transparent human-machine collaborative decision-making”, Examiner respectfully disagree. Examiner asserts that the claims are given their broadest reasonable interpretation for the purpose of determining whether they encompass a judicial exception. The claim(s) as amended, under BRI, recite a process for collecting and analyzing clinical data parameters and risk index values to predict results and comparing to a clinical range values and generating risk information and trust levels corresponding to each parameter(s) analyzed and present out comes for decision making, while implemented on a computer explainable program and machine learning, to perform the steps, which recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. “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." see MPEP § 2106.04(a)(2)(III)(C).
Furthermore, the Applicant argues that the judicial exception is integrated into a practical application as described by the Applicant “(e.g., comparing the parameters and the risk indexes to generate a risk information)”, where the comparing parameters and risk index to generate risk information is part of the identified abstract idea steps and not as alleged providing a practical application. Similarly, the Applicant argues that the claims as a whole is significantly more as described by the Applicant “(generate a determining result according to the parameters, the trusting levels, and determine whether to adjust a medical decision according to the determining result)”, where the adjusting a medical decision based on results is a step of the identified attract idea. As described in the rejection above, the claim does not describe a particular improvement of computer’s functionality or a technical field, rather using additional elements, “e.g., processor, machine learning model”, to perform the steps abstract idea such as obtaining, analyzing, comparing, and visualizing/displaying information through leveraging computing technology in a well understood manner however improving upon an abstract idea does not make the abstract idea any less abstract.
In light of the Alice decision and the guidance provided in the 2019 PEG, the features listed in the claims, are not considered an improvement to another technology or technical field, or an improvement to the functioning of the computer itself rather describes an improvement to decision making, which is solving a health facility and administrative problem, using computers. However, improving upon an abstract idea does not make the abstract idea any less abstract. In addition, by relying on computing devices to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible (See Alice, 134 S. Ct. at 2359 "use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions" is not an inventive concept).
Therefore, the Applicant argument is found to be unpersuasive and Examiner remains the 101 rejections of claims which have been updated to address Applicant's argument.
Applicant's arguments with respect to the 35 U.S.C. § 103 rejection on page 13-16.
In response to the Applicant claim(s) amendment and argument/remarks, Examiner withdraws the prior art rejection, see “Claims Free of Prior Art” above.
Response to Amendment
Applicant's arguments filed 02/18/2026 have been fully considered by the Examiner and addressed as the following:
In the remarks, Applicant argues in substance that:
Applicant's arguments with respect to the 35 U.S.C. § 101 rejection on page 8-11.
On page 9 of the remarks, the Applicant argues “The claims recite specific computer-implemented mechanisms and data structures that cannot be performed "in the human mind .... These features collectively require non-trivial computational orchestration across many locations and horizons, driven by operator-configurable hyperparameters and module-defined flows...”, Examiner respectfully disagree. Examiner asserts that the claims are given their broadest reasonable interpretation for the purpose of determining whether they encompass a judicial exception. The claims, under BRI, recite an abstract idea which have been analyzed under Step 2A, Prong One reciting a process for selecting and generating ensemble models using weight schemes for a plurality of features for predicting disease risk, execute different combination of schemes during a period of time and select the best prediction model providing results and perform an action to the predictive results, which are steps of observing, evaluating, judgment, and opinion that are citing a process for which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III), but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for which both the instant claims and the abstract idea are defined as Mental Process. In addition, Examiner also finds no disclosure of any hyperparameters used for configurating the data flow neither model parameters.
On page 9-10 of the remarks, the Applicant argues “Moreover, the claim focus is a specific improvement to ML ensemble operation and forecasting precision. The specification expressly identifies a technical problem ... and presents the claimed dynamic precision ensemble selection as a technical solution (more accurate, time-stamped, locality-level predictions). Under Desjardins and Enfish, such software/ML improvements are non-abstract when they enhance system operation”, Examiner respectfully disagree. As mentioned above, the steps recited in independent claims, when viewed as a whole, recite a Mental process and the recitation of machine learning model have been analyzed under Step 2A, Prong Two as an additional element cited as a tool (e.g., machine learning) for implementing claim steps that amounts to no more than mere instructions to implement “apply” the exception using a generic computer component and no more than adding the words "apply it" (or an equivalent) with the judicial exception. There is no specific process recited in the claim(s) other than what is understood in data selecting and generating a model to measure performance and evaluated by schemes applied and inputting data into using machine learning (ML) computing components recited at a high level of generality such that leveraging generic computing functionality.
Furthermore, the Applicant argues Desjardins (see Ex parte Desjardins), quoting (“ML claims and directed examiners to evaluate ML-related improvements under Enfish (and McRO), recognizing technology-specific architecture and data structures as eligible when they improve system operation."” The Appeals Review Panel (ARP) found the Desjardins claims to be directed to methods for training artificial intelligence/machine learning (AI) models and the claims improved the functioning of the computer itself, as such it is not “directed to” an abstract idea under Alice Step 1. Similarly, the improvement in Enfish, for example, provided an improvement to a computer function and/or technical field (self-pointing database) reciting a self-referential table for a computer database providing a particular improvement in the computer’s functionality that improves the way a computer stores and retrieves data in memory. In contrast, the instant claim(s) and specifications do not recite an improvement to technology, as in Enfish, nor Desjardins, as appealed, but to performance of an abstract idea such as modeling and predicting a future disease while using well-known computing system and components.
On page 10 of the remarks, the Applicant argues “Even if a judicial exception were implicated, the claims integrate it into a practical application through multiple meaningful limitations, precisely the type Desjardins and the updated MPEP instruct examiners to recognize... the claims involve concrete data structures and a concrete architecture. The claimed scheme combination objects and combination vectors are Enfish-style logical structures ... This structured process improves the operation of the ML forecasting system-the essence of a practical application per Desjardins ... it is closed-loop control impacting computing and operational systems”, Examiner respectfully disagree to these arguments. As mentioned above, the Desjardins claims are directed to methods for training artificial intelligence/machine learning (AI) models and the claims improved the functioning of the computer itself. While the Applicant claims recite steps for generating a prediction of future risk of infection using machine learning models, the claims do not positively recite any steps for training the machine leaning model as such no vectors are described nor closed-loop operation.
On page 11 of the remarks, the Applicant argues “Under Step 2B, the ordered combination of limitations recited in claims 1, 19, and 20 provides an inventive concept, particularly in light of Desjardins' emphasis on recognizing ML-specific architectural improvements.”, Examiner respectfully disagree. Examiner points to the remarks and while the claimed invention describes a use of machine learning models, there is no description of how the model is trained rather the claim(s) disclosing the machine learning model at a high level of generality as generic computing components and as a tool such recited and not configured in a manner other than what any off-the-shelf, commercially available processor is capable of being programmed for performing generic computer functions in relation to an abstract concept.
Applicant further argues that “The Office Action provides no evidence that this specific arrangement was well-understood, routine, or conventional, which is required under Berkheimer to sustain a Step 2B rejection”, Examiner respectfully disagree to the argument. Examiner asserts that the reach of Berkheimer, 881 F.3d 1360 (Fed. Cir. 2018), in this case is limited, because the only alleged unconventional feature in the Applicant claims is simply restates what already determined is an abstract idea. Since the claim(s) limitations did not recite steps to train machine learning model as such were unconventional, there was no error in finding that the claims lacked an inventive concept. In addition, the Examiner described that the claims at issue do not require any nonconventional computer, network, or other components, or even a non-conventional and non-generic arrangement of known, conventional pieces but merely call for performance of the claimed functions on a set of generic computer components.
Therefore, the Applicant argument(s) is/are not found to be persuasive. Hence, Examiner remains the 101 rejections of claims which have been updated to address Applicant's amendments.
Applicant's arguments with respect to the 35 U.S.C. § 103 rejection on page 11-12.
In response to the Applicant claim(s) amendment and argument/remarks, Examiner withdraws the prior art rejection, see “Claims Free of Prior Art” above.
Prior Art Cited but not Applied
The following document(s) were found relevant to the disclosure but not applied:
US 2024/0274291 “Basu” discloses predicting changes in risk based on intervention information for a patient and update intervention ion information and generate a list.
The references are relevant since it discloses training a ML model for predicting a future risk.
Conclusion
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/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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