DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
Receipt of Applicant’s Claims filed July 10, 2026 is acknowledged.
Response to Amendment
Claims 1, 7-10, 13, and 19-20 have been amended. Claims 4-5, 11-12, and 16-17 have not been modified. Claims 2-3, 6, 14-15 and 18 have been cancelled. Claims 1, 4-5, 7-13, 16-17, and 19-20 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments with respect to Remarks filed on July 10, 2026 have been considered but are not fully persuasive. Response has been provided below.
Applicant argues 35 U.S.C. §101 Rejection, starting pg. 11 of Remarks:
Regarding A, Applicant argues that the amended claim limitations of generating a model by applying weights or Bayesian probabilities to identified predictor variables based on the identified associations and subsequently performing back propagation to update the predictive model based on the comparison of the updated hypotheses and the initial hypotheses through adjusting the variables provides an improvement to conventional back propagation methods when both generating and updating the pandemic infection model.
Examiner respectfully disagrees. The amended claim limitations are primarily directed towards applying mathematical operations to the generation and updating of pandemic infection models. Specifically, the claim limitations of “generating the predictive model by applying weights or Bayesian probabilities to the identified predictor variables based on the identified associations”, “performing back propagation to update the predictive model based on the comparison of the updated hypotheses and the initial hypotheses”, and “updating the weights or Bayesian probabilities of the predictive model based on the adjusted associations and applying the updated weights or Bayesian probabilities to the adjusted predictor variables” are applying known mathematical operations (weights, Bayesian probabilities, back propagation) towards a field of use in generating and updating pandemic models; MPEP 2106.05(h): “Examples of limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception include: vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)” Similarly, the generating a pandemic infection model is a field of use of the core technology of generating a machine learning model using conventional means.
Furthermore, Applicant specification fails to note wherein the methods used provide a technical improvement over previously known machine learning model generation and updating methods. [0070] of Applicant specification recites: “the predictive model 242 predicts the future spread of the disease based on predictor variables identified in the data 214 (e.g., numerical metrics, Boolean conditions, etc.) and associations (e.g., weights, Bayesian probabilities, etc.) between those predictor variables and the future spread of the disease.”, which describes a general application of mathematical processes to achieve model generation. Likewise, the only recitation of back propagation is provided in [0071], which simply recites: “In either embodiment, providing the machine learning module 240 with the difference between the updated hypotheses 268' and the initial hypotheses 268 enables the machine learning module 240 to perform back propagation and readjust the predictor variables and associations (and/or the model structure, initial conditions, boundary conditions, etc.) to make the predictive model 242 represent and classify the current state of knowledge.” No specific, technical improvements are provided to how the machine learning functions.
Support that no technical improvements are being made to the training and updating processes is found [0033] of Applicant specification, which recites: “The machine learning module 240 may utilize any or all supervised, unsupervised, or semi-supervised learning approaches.”
Regarding B, Applicant argues that the prior Office Action fails to consider the combination of hypothesis generation and predictive modeling and that the claims recite a particular solution to a particular problem, not merely the idea of applying machine learning. Examiner respectfully disagrees.
The claims have been, and continue to be, analyzed under the proper 35 U.S.C. 101 analysis as outlined under MPEP 2106. Additionally, the particular solution to the particular problem is abstract in nature, not technical, as it is directed towards generating pandemic models to predict disease spread based on hypotheses about the disease.
Regarding C(1), Examiner did not agree that the claims do not fall into the mathematical concepts grouping. Only that now cancelled claim 6 did not, as was under the Topic for Discussion in the Automated Interview Request submitted on July 2, 2026. The independent claims are characterized under an abstract idea of mathematical concepts for generating dimensional spaces, as previously noted in the Non-Final Rejection provided on March, 10, 2026, and for the now amended claim limitations of “generating the predictive model by applying weights or Bayesian probabilities to the identified predictor variables based on the identified associations”, “performing back propagation to update the predictive model based on the comparison of the updated hypotheses and the initial hypotheses”, and “updating the weights or Bayesian probabilities of the predictive model based on the adjusted associations and applying the updated weights or Bayesian probabilities to the adjusted predictor variables”, which explicitly recite performance of mathematical operations.
Regarding C(2), the claims were not and continue to not be characterized under mental processes.
Regarding C(3), Applicant argues that the claims do not fall under a certain method of organizing human activity as pandemic infection modeling does not fall under any of the sub-groupings. Examiner respectfully disagrees.
The claims are characterized under certain method of organizing human activity as managing personal behaviors not because the generated information is merely useful to epidemiologists or public health officials, but because pandemic infection modeling is an activity that is regularly performed by epidemiologists and public health officials. Machine learning is only applied to perform this activity through an automatic means. Thus, the claims are abstract for managing the personal behaviors of said epidemiologists and public health officials to generate hypotheses and perform pandemic infection modeling.
Regarding D, Applicant argues that the claims mirror the fact pattern of McRO as it is directed to a computer system that enables human public health researchers to model a pandemic infection by replacing a subjective process with a specific, rule-based technical process that prior human processes did not employ. Examiner respectfully disagrees.
Unlike McRO, the proposed process of instant application is directed towards an abstract process rather than a technical one. For example, generating hypotheses from data is an activity that is typically employed by researchers when determining new topics to study. Furthermore, pandemic modeling is an activity that is routinely performed by epidemiologists who study disease spread. The claimed process applies mathematical processes (N-dimensional ontology space, ranking of hypotheses, weights or Bayesian probabilities, back propagation) and conventional machine learning techniques (identifying predictor variables, identifying associations, generating a model using weights or Bayesian probabilities, adjusting variables based on new data, updating the weights or Bayesian probabilities) to a computing device to improve upon the performance of the abstract idea of generating hypotheses and pandemic modeling without improving the underlying technology itself (machine learning model).
The fact patterns of the instant application mirror claim 2 of Example 47, which noted:
The recitation of “using a trained ANN” in limitations (d) and (e) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained ANN” limits the identified judicial exceptions “detecting one or more anomalies in a data set using the trained ANN” and “analyzing the one or more detected anomalies using the trained ANN to generate anomaly data,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Similarly, the use of machine learning and limiting its use to the judicial exceptions of hypothesis generation, ranking, and subsequent pandemic infection modeling only serves to confine the use of the abstract idea to a particular technological environment of machine learning.
Regarding E(1), Applicant argues that the claims may be directed towards an improvement in “other technology of technical field”, not just improvements to the functioning of computers. Examiner agrees.
Regarding E(2), Applicant argues that the claims improve the neural network-enabled computing systems used to perform pandemic infection modeling. Examiner respectfully disagrees.
Here, the neural network-enabled computing system is applied to improve upon performance of the abstract ideas of hypothesis generation and pandemic modeling by human means. MPEP 2106.05(f)(2) notes: “"claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).”
No specific, technical improvements are made to neural network-enabled computing systems as the claims provide mere instructions to implement an abstract idea or other exception on a computer.
Regarding E(3), Applicant submits that many cases have been patent eligible under §101 by ikmproving the accuracy of the output of a technical process. Examiner agrees.
However, the instant application is dissimilar in that the increase in accuracy in the resulting pandemic model is due to the increased efficiency inherent with applying the abstract idea on a computer, as noted above, and does not improve technology in the same way as cases such as McRO, CardioNet, Thales Visionix, and Koninklijke.
Regarding E(4), Applicant submits that the key distinction is whether the claimed system improves the underlying technical process or merely provides new/better data to existing machine learning systems. Examiner agrees.
However, Examiner notes that the instant application merely provides new/better data to existing machine learning systems rather than improving upon the underlying technical process.
Regarding E(5), Applicant argues that the multitude of features that are used to generate the pandemic infection model and the multitude of features that are used to update the pandemic model do not teach the process “at a high level of generality”. Examiner respectfully disagrees.
The only limitations describing generation of the pandemic model are the claim limitations “using the initial data to train a machine learning module to generate a predictive model of a pandemic infection, the predictive model generating an initial prediction of how a disease will spread in one or more locations, the machine learning module generating the predictive model by:” and “generating the predictive model by applying weights or Bayesian probabilities to the identified predictor variables based on the identified associations”, which teaches a generic process of training a model using mathematical means. No specific, technical improvements are made to how machine learning models are trained, as the training process claimed is generic and does not provide an improvement to the training process. Support is found in [0033] of Applicant specification, which recites: “The machine learning module 240 may utilize any or all supervised, unsupervised, or semi-supervised learning approaches.” The preceding steps of “identifying an ontology”, “generating an N-dimensional ontology space”, “receiving initial data”, and “using the initial data to generate and rank initial hypotheses regarding a pandemic infection” are abstract activities that only serve to inform the variables and training and have no bearing on the technical process of how the training itself is performed.
Similarly, the updating process is described at a high level of generality (“performing back propagation to update the predictive model based on the comparison of the updated hypotheses and the initial hypotheses by:” and “updating the weights or Bayesian probabilities of the predictive model based on the adjusted associations and applying the updated weights or Bayesian probabilities to the adjusted predictor variables”). The described methodology is a generic process of retraining the model using updated data using mathematical means that does not provide an improvement over other iterative training methods available. The prior steps of “receiving updated data”, “using the updated data to generate updated hypotheses…”, “assigning an updated ranking to each of the updated hypotheses…”, and “comparing the updated rankings of the updated hypotheses to the initial rankings of the initial hypotheses…” are abstract activities that only serve to inform the identified iterative training steps.
Thus, Examiner maintains that the claimed processes of generating a pandemic infection model and updating the model are taught at a high level of generality.
Regarding E(6), Applicant argues that the claimed system improves the underlying process similar to Koninklijke to improve the accuracy of the output by detecting persistent errors, citing [0075-0076] of Applicant specification.
Examiner notes that the errors identified by [0075-0076] of Applicant specification are errors due to the assumptions, which are errors attributable to the abstract idea of hypothesis generation, not errors attributable to a technical issue in the machine learning itself. Examiner maintains that the improvement is the abstract idea of hypothesis generation and pandemic modeling, and the improvements to accuracy are only a result of the “efficiency inherent with applying the abstract idea on a computer”, which does not integrate a judicial exception into a practical application or provide an inventive concept; MPEP 2106.05(f)(2).
As previously stated, the neural network is taught at a high level of generality such that it provides nothing more than mere instructions to implement an abstract idea on a generic computing device; see MPEP 2106.05(f).
Regarding F, Applicant argues that “conventional” pandemic modeling systems do not drive back propagation of predictor variables and associations using multi-dimensional ontology spaces, ontological vectors, or a hypothesis-space comparison nor is using the claimed neural network module for both generating and updating the claimed pandemic infection model “well understood”.
However, the consideration under Step 2B is if the additional elements, alone or in combination, (additional elements in claim 1: predictive model, training the machine learning module, machine learning module, updating the predictive model) are well-understood, routine and conventional in the field – the novelty of the abstract idea is not considered relevant under the Step 2B analysis. Here, the additional elements, alone or in combination, amount to instruction to implement the abstract ideas of hypothesis generation and pandemic infection modeling using a general purpose computer and conventional machine learning methods. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Regarding G, Examiner thanks Applicant for the reminder. However, the instant application, as currently claimed, does not constitute a close call.
Regarding neural networks
Although many of Applicant’s arguments are directed towards neural networks, the claims are not limited to neural network implementation as none of the claims recite neural networks. Instead, the predictive model may encompass any machine learning model, including machine learning implementations that may be easily performable using human methods, such as linear regression.
Support exists in [0031] of Applicant specification, which recites: “The machine learning module 240 may utilize approaches that include classification, regression, regularization, decision-tree, Bayesian, clustering, association, neural networks, deep learning algorithms, etc. Deep learning algorithms may include recurrent models, convolutional models, transformer models with or without attention, etc. The machine learning module 240 may employ various machine learning algorithms known in the art, for instance pre-train transformers (used as global data), one or more final layers (trained while maintaining previous layers for localization),18 etc.”
However, Examiner has responded to arguments as if the machine learning module recited in the independent claims is directed towards neural networks to address the intent of Applicant’s arguments.
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 1, 4-5, 7-13, 16-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a process and a machine
(claims 1, 4-5, 7-13, 16-17, and 19-20). Accordingly, claims 1, 4-5, 7-13, 16-17, and 19-20 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, 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).
The Examiner has identified method Claim 1 as the claim that represents the claimed invention for analysis, and is similar to system claim 13.
Claim 1:
A method for generating and updating a predictive model of a pandemic infection by identifying hypotheses in initial data and testing the identified hypotheses using updated data, the method comprising:
identifying an ontology having N elements, each element having a plurality of ontological terms, each combination of ontological terms from each of the N elements of the ontology forming an ontological vector generating an N-dimensional ontology space, wherein:
each of the N dimensions corresponds to one of the N elements of the identified ontology; and
similar or related ontological terms are closer together in the N-dimensional ontology space than dissimilar and unrelated ontological terms
receiving initial data from a plurality of data sources;
using the initial data to generate and rank initial hypotheses regarding a pandemic infection by:
extracting ontological terms from the initial data and generating a plurality of ontological vectors by mapping the extracted ontological terms from the initial data to the N-dimensional ontology space;
grouping the ontological vectors to form groups of ontological vectors, each group of ontological vectors forming an initial hypothesis;
weighting each group of ontological vectors at least in part based on the frequency of occurrence of the ontological vectors in the initial data; and
assigning an initial ranking to each of the initial hypotheses by using an optimization algorithm to rank the groups of ontological vectors identified in the initial data in accordance with the weight of each group of ontological vectors;
using the initial data to train a machine learning module to generate a predictive model of a pandemic infection, the predictive model generating an initial prediction of how a disease will spread in one or more locations, the machine learning module generating the predictive model by:
identifying predictor variables in the initial data;
identifying associations, based on the initial hypotheses, between the identified predictor variables and the spread of the disease; and
generating the predictive model by applying weights or Bayesian probabilities to the identified predictor variables based on the identified associations;
receiving updated data;
using the updated data to generate updated hypotheses by identifying the groups of ontological vectors in the updated data and ranking the ontological vectors identified in the updated data;
comparing the updated rankings of the updated hypotheses to the initial rankings of the initial hypotheses by:
identifying an updated hypothesis having a higher updated ranking than the initial ranking of the initial hypothesis corresponding to the same group of ontological vectors; or
identifying an initial hypothesis having a higher initial ranking than the updated ranking of the updated hypothesis corresponding to the same group of ontological vectors;
performing back propagation to update the predictive model based on the comparison of the updated hypotheses and the initial hypotheses by:
adjusting the identified predictor variables to reflect the updated hypotheses;
adjusting the identified associations between the identified predictor variables and the spread of the disease to reflect the updated hypotheses; and
updating the weights or Bayesian probabilities of the predictive model based on the adjusted associations and applying the updated weights or Bayesian probabilities to the adjusted predictor variables; and
using the updated predictive model to generate an updated prediction of how the disease will spread in the one or more locations.
These claims recite an abstract idea of: mathematical processes. The claim recites “generating an N-dimensional ontology space,…”, “generating a plurality of ontological vectors by mapping the extracted ontological terms from the initial data to the N-dimensional ontology space”, “weighting each group of ontological vectors at least in part based on the frequency of occurrence of the ontological vectors…”, “assigning an initial ranking to each of the initial hypotheses by using an optimization algorithm…”, “applying weights or Bayesian probabilities…”, “assigning an updated ranking to each of the updated hypotheses…”, “performing back propagation…”, and “updating the weights or Bayesian probabilities…”. Generating dimensional spaces is a mathematical representation of the relationships between data points, as supported by [0043] of Applicant specification “The populated ontology space 346 is a geometric representation of possible events that are encoded by that particular corpus of data 214 according to that particular ontology 324.” [0047] of Applicant specification recites: “it is a moderately well- defined optimization problem that can be solved using an iterative optimization algorithm (such as coordinate or gradient descent) or a heuristic optimization algorithm (such as simulated annealing, a Monte Carlo-based algorithm, a genetic algorithm, etc.).” which teaches wherein the optimization algorithm comprises several different mathematical processes to optimize data. Furthermore, the operations of applying/updating weights or Bayesian probabilities and back propagation explicitly recite mathematical concepts.
These above limitations, under their broadest reasonable interpretation, also cover performance of the limitation as certain methods of organizing human activity under managing personal behaviors of people. The claim elements are directed towards generating and ranking the ontological vectors representing hypotheses identified in the data, which [0011] of Applicant’s specification is in order “to combine uncertain information based on early observations with new observations as disease spreads to new areas to avoid being misled and surprised”. Managing observations of people in the form of ranking, which provides a recommended target of study, modifies the personal behaviors of the people involved in relevant research fields. The claims further recite “the predictive model generating an initial prediction of how a disease will spread in one or more locations” using identified predictor variables and associations and “using the updated predictive model to generate an updated prediction of how the disease will spread in one or more locations”, which teaches an abstract idea of infectious disease modeling, which is a human activity typically performed by epidemiologists.
Accordingly, the claim recites at least one abstract idea.
Claim 13 is abstract for similar reasons.
Subject Matter Eligibility Criteria – 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 idea into a practical application. As noted at MPEP §2106.04 (ID)(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 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 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).
Additional elements cited in the claims:
predictive model (1,7-10,13,19-20); machine learning module (1,8,13,16,20); train a machine learning module (1,13); update the predictive model (1,13); update one or more computer networks (11); system (13,16-17,19-20); data collection module (13); hypothesis generation module (13,16); hypothesis space difference evaluation module (13)
Any computing devices (system) would be able to perform the method and the associated software modules that are used within the computing environment (data collection, hypothesis generation, hypothesis space difference evaluation modules) are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0021] of Applicant specification recites: “FIG. 1, the architecture 100 may include a server 120 that communicates with client devices 180, for example via one or more networks 130 such as the Internet. The server 120 includes one or more hardware computer processors 160 and non-transitory computer readable storage media 140. The server 120 receives data 212 from data sources 110. The server 120 may be any suitable computing device including, for example, an application server or a web server.” [0022] of Applicant specification recites: “FIG. 2 is a block diagram illustrating the system 200, which is realized by software modules executed by the hardware computer processor(s) 160, generating and distributing initial hypotheses 268 and an initial prediction 246 according to an exemplary embodiment.” No specific, technical improvements are being made to computing devices as generic devices with software modules are simply being used to perform the abstract ideas of hypothesis generation and pandemic modeling.
Machine learning (machine learning module, predictive model) and its training (train a machine learning module, update the predictive model) are also taught at a high level of generality. [0031] of Applicant specification recites: “The machine learning module 240 may utilize any or all supervised, unsupervised, or semi- supervised learning approaches. The machine learning module 240 may utilize approaches that include classification, regression, regularization, decision-tree, Bayesian, clustering, association, neural networks, deep learning algorithms, etc. Deep learning algorithms may include recurrent models, convolutional models, transformer models with or without attention, etc. The machine learning module 240 may employ various machine learning algorithms known in the art, for instance pre-train transformers (used as global data), one or more final layers (trained while maintaining previous layers for localization),18 etc.” No specific, technical improvements are being made to the field of machine learning as any generic machine learning algorithm applied to perform the abstract idea of predicting spread of disease.
Computer networks are also taught at a high level of generality. [0021] of Applicant specification recites: “As shown in FIG. 1, the architecture 100 may include a server 120 that communicates with client devices 180, for example via one or more networks 130 such as the Internet.” No specific, technical improvements are being made to the field of computer networking as the Internet is applied to perform the insignificant extra-solution activity of transmitting data.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 4 and 16: These claims recite wherein using the optimization algorithm to rank the weighted groups of ontological vectors comprises: using a first optimization function to perform a coarse ranking of the weighted groups of ontological vectors and identify a subset of the highest ranked groups; and using a second optimization function to perform a precise ranking of the subset of groups ranked highest by the first optimization function; which further limits usage of the algorithms to perform the abstract idea of ranking the ontological vectors.
Claims 5 and 17: These claims recite wherein the optimizing algorithm includes a heuristic optimization function or an iterative optimization function; which only serves to further limit the type of algorithm.
Claims 6 and 18: These claims recite the method further comprising: using the initial data to train a machine learning module to generate a predictive model of a pandemic infection, the predictive model generating an initial prediction of how a disease will spread in one or more locations; updating the predictive model based on the comparison of the updated hypotheses and the initial hypotheses; and using the updated data and the updated predictive model to generate an updated prediction of how the disease will spread; which teaches an abstract idea of certain methods of organizing human activity, such as predicting how a disease will spread in one or more locations, which is a human activity typically performed by epidemiologists. This claim further teaches iterative training at a high level of generality, such that no specific, technical improvements are being made to the field of machine learning.
Claims 7 and 19: These claims recite wherein the predictive model is updated by reducing a weight or probability previously applied to an identified predictor variable corresponding to an initial hypothesis having a higher initial ranking than the updated ranking of the corresponding updated hypothesis; which teaches an abstract idea of mathematical concepts by reducing the weight or probability of a less relevant hypothesis.
Claims 8 and 20: These claims recite wherein the machine learning module further adjusts the predictive model by learning additional predictor variables indicative of the updated hypotheses and/or learning additional associations, reflected in the updated hypotheses, associations between the identified predictor variables and the spread of the disease; which teaches an abstract idea of identifying additional predictor variables and/or adjusted associations between variables and spread of disease, which is a human activity routinely performed by epidemiologists. This claim further teaches updating training the machine learning at a high level of generality, such that no specific, technical improvements are made to how machine learning models are trained.
Claim 9: This claim recites wherein the associations used by the predictive model comprise weights or Bayesian probabilities; which only serves to limit the type of associations. This claim further teaches an abstract idea of mathematical processes.
Claim 10: This claim recites wherein the predictor variables used by the predictive model comprise numerical values or Boolean conditions; which only serves to limit the variables. This claim further teaches an abstract idea of mathematical processes.
Claim 11: This claim recites the method further comprising: outputting, for transmittal via one or more computer networks: the updated hypothesis having a higher updated ranking than the initial ranking of the initial hypothesis corresponding to the same group of ontological vectors; or the initial hypothesis having a higher initial ranking than the updated ranking of the updated hypothesis corresponding to the same groups of ontological vectors; which only serves to limit the hypothesis output. This claim further teaches the computer network(s) at a high level of generality, such that they are only applied to perform the insignificant extra-solution activity of transmitting data.
Claim 12: This claim recites wherein: the updated hypothesis having a higher updated ranking than the initial ranking of the initial hypothesis corresponding to the same group of ontological vector represents a potential new insight regarding the pandemic infection; or the initial hypothesis having a higher initial ranking than the updated ranking of the updated hypothesis corresponding to the same group of ontological vectors represents a previous assumption regarding the pandemic infection; which only serves to narrow the abstract idea of the ranking of the hypotheses.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims 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 reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 4-5, 7-12 16-17, and 19-20 additional limitations which amount to elements that have been recognized as activities in particular fields, claims 4-5, 7-12 16-17, and 19-20, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 4-5, 7-12 16-17, and 19-20, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. 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.
Therefore, whether taken individually or as an ordered combination, claims 1, 4-5, 7-13, 16-17, and 19-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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