DETAILED ACTION
Applicant’s response, filed 11 November 2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Claim Status
Claims 21-39 and 41 are pending and examined herein.
Claims 21-39 and 41 are rejected.
Priority
Claims 21-39 and 41 are granted the claim to the benefit of priority to PCT/US2008079674 filed 12 October 2008. Thus, the effective filling date of claims 21-39 and 41 is 12 October 2008.
Claim Objections
The objection of claims 21 and 35 in Office action mailed 11 July 2025 is withdrawn in view of the amendments of “a n-dimensional vector valued function” (in claims 21 and 35), “the n-dimensional vector-valued function” (in claim 35), and removing the language of “at leas tone” received 11 November 2025.
Claim Interpretation
Bidirectional mapping between the data of medical condition metrics for a respective other patient and at least one individual component associated with the respective other patient’s disease is interpreted as data metrics being associated with a particular patient.
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.
The rejection below has been modified necessitated by amendment.
Claims 21-39 and 41 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.
(Step 1)
Claims 21-34 are directed to a method and Claims 35-39 and 41 are directed to machine.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. Along with abstract ideas of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
Independent claims 21 and 35 recite a mental process of “generate a pathological model based on the self-reported information of the plurality of other patients…”, “generate and train a predictive model comprising a transition matrix for the individual patient for the identification of a progression of a disease…”, “wherein the transition matrix is indicative of a decay of the at least one individual component based at least in part on the pathological model”, “generate, using the predictive model… at least one disease state prediction for the individual patient…”, “mapping, via the bidirectional mapping provided via the pathological model, the at least one patient-specific patient provided value…”, “re-fitting the predictive model based on the n-dimensional vector-valued function for the individual patient for subsequent iterations for the individual patient to determine a transition matrix of the predictive model”, “determine a plurality of errors, wherein each error is between the at least one disease state prediction and the at least one patient-provided value of the plurality of patient provided values…”, “determine a distribution of errors…”, “determine the confidence interval of the at least one state prediction into a set of confidence bands…”, “transform the determine confidence interval of the at least one state prediction into a set of confidence bands…”, and “an indication of at least one efficacy, need, or both for a particular treatment based at least in part on the pathological model and the bidirectional mapping”.
Independent claims 21 and 35 recite mathematical concepts of “generate a pathological model based on the self-reported information of the plurality of other patients…”, “generate and train a predictive model comprising a transition matrix for the individual patient for the identification of a progression of a disease…”, “wherein the transition matrix is indicative of a decay of the at least one individual component based at least in part on the pathological model”, “generate, using the predictive model… at least one disease state prediction for the individual patient…”, “mapping, via the bidirectional mapping provided via the pathological model, the at least one patient-specific patient provided value…”, “re-fitting the predictive model based on the n-dimensional vector-valued function for the individual patient for subsequent iterations for the individual patient to determine a transition matrix of the predictive model”, “determine a plurality of errors, wherein each error is between the at least one disease state prediction and the at least one patient-provided value of the plurality of patient provided values…”, “determine a distribution of errors…”, “determine the confidence interval of the at least one state prediction into a set of confidence bands…”, “transform the determine confidence interval of the at least one state prediction into a set of confidence bands…”.
Dependent claims 22 and 41 recite a mental process of “determine a prediction concerning an effect of an intervention for the individual patient…”. Dependent claims 25 and 37 recite a mental process of “process a request from the individual patient to modify a composition of a subset of patients…”. Dependent claims 26 and 38 recite a mental process of “calculate a confidence interval for the distribution of errors” and “select, based on the confidence interval for the distribution of errors, a set of patient-provided values…”. Dependent claims 26 and 38 recite a mathematical concept of “calculate a confidence interval for the distribution of errors”. Dependent claim 27 further recites a mental process and mathematical process of “calculate a predicted value…”, “calculate an error between the predicted value…”, “determine the distribution of the errors”, and “calculate the confidence interval from the distribution”. Dependent claim 39 recites a mental process and mathematical concept of “determining the distribution of errors for a plurality of individual patients”.
The claims recite steps of analyzing/evaluating data and organizing data as “generate a pathological model based …”, “generate and train a predictive model comprising a transition matrix …”, “wherein the transition matrix…”, “generate, using the predictive model…”, “mapping, via the bidirectional mapping…”, “re-fitting the predictive model…”, “determine a plurality of errors …”, “determine a distribution of errors…”, “determine the confidence interval…”, “transform the determine confidence interval …”, and “an indication of at least one efficacy, need, or both for a particular treatment based at least in part on the pathological model and the bidirectional mapping”. The human mind is capable of analyzing/evaluating numerical data by building models (and/or using mathematical processes) and determining numerical errors based on predictions. The human mind is capable of organizing numerical data by organizing data into subsets (i.e. subsets of populations and training data), making selections from the data, and plotting numerical values using confidence intervals. The claims recite mathematical calculations as “generate a pathological model based …”, “generate and train a predictive model comprising a transition matrix …”, “wherein the transition matrix…”, “generate, using the predictive model…”, “mapping, via the bidirectional mapping…”, “re-fitting the predictive model…”, “determine a plurality of errors …”, “determine a distribution of errors…”, “determine the confidence interval…”, “transform the determine confidence interval …” by using statistical models (i.e., Markov models with a mathematical transition matrix see instant disclosure page 19), a mathematical model (i.e., a mathematical construct which is generated and implemented as a nodes with values and edges with weight values connecting the nodes) (see instant disclosure page 20), refitting models through updating numerical values utilizing mathematical calculations, determining errors, determining a distribution of errors and determining confidence errors which are statistical calculations and transforming the confidence interval into a prediction utilizing mathematical calculations (see instant disclosure pages 18 and 21) because these steps recite the performance of mathematical processes that intake numeric values and produce numerical values. Thus claims 21-39 and 41 recite abstract ideas. Dependent claims 23, 24, and 28-34 further limit the mental process/mathematical concept recited in the independent claim but do not change their nature as a mental process/mathematical concept.
(Step 2A Prong 2)
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional elements in claims 21, 22, 25, 27, 35-37, and 41 of a server coupled, via a network, to computers, interactive graphical user interfaces for receiving data input, receiving data into a computer environment, and displaying data do not integrate the judicial exceptions into a practical application because this is adding insignificant extra solution activity of data gathering and outputting data see MPEP 2106.05(g). These additional elements are data gathering steps because they interact with the judicial exceptions by providing data for the judicial exceptions to process and outputting the results of the judicial exceptions on a display.
The additional element in claim 21 and 35 of using a generic computer to perform judicial exceptions and utilizing a neural network does not integrate the judicial exception into a practical application because this is simply applying the exception to a generic computer without an improvement to computer technology and mere instructions to apply the exception to a generic computer see MPEP 2106.04(d)(1) and MPEP 2106.05(f).
Thus, the additional elements do not integrate the judicial exceptions into a practical application and claims 21-35 and 41 are directed to the abstract idea.
(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 because:
The additional elements in claims 21, 22, 25, 27, 35-37, and 41 of a server coupled, via a network, to computers, receiving data into a computer environment are conventional which is shown by the server coupled with computers via a computer network (figure 3 and [0029]), an user interface ([0040]), receiving data ([0005] and [0029]), and displaying data ([0044]) in Fey et al. (US 20070143151 A1; previously cited) and the server coupled with computers via a computer network ([0065]), an user interface ([0112]), receiving data ([0079] and [0082]), and displaying data ([0064], [0079], and [0094]) in Peswtotnik et al. (US 20040260666 A1; previously cited).
The additional element in claim 21 and 35 of using a generic computer to perform judicial exceptions is conventional see MPEP 2106.05(b) and MPEP 2106.05(d)(II).
Thus, the additional elements (alone or in combination) do not amount to significantly more than the judicial exception.
Response to Arguments
Applicant's arguments filed 11 November 2025 have been fully considered but they are not persuasive.
Argument 1:
Applicant argues that the claims recites a particular technical improvement whereby combining the particular data structure of the bidirectional mapping of a plurality of patients with a transition matrix for an individual patient can be used to enable personalized disease state prediction and prediction of treatment efficacy and/or need (Reply p. 12-15). Applicant argues that the claim improves the technology of machine learning-based disease state modeling and treatment (Reply p. 12-15).
This argument has been fully considered but found to be not persuasive. The MPEP states at 2106.05(a) “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements… In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception”. The determination of an improvement to technology has two steps, the identification of additional elements (which define the technology) and the evaluation of the additional elements to determine if the improvement is realized in the additional elements either by the additional elements themselves or the additional element in combination with the judicial exception (i.e. the interaction between the judicial exceptions and the additional elements). It is noted that the pathological model encompasses a mathematical construct of a graph with weights on the edges of the graph and values of the nodes of the graph (see instant disclosure page 19) which is part of the abstract idea, the predictive model encompasses a Markov model (or Hidden Markov model) which is trained to determine an optimal transition matrix (which is a matrix holding probabilities) describing the decay of a component in the pathological model (see instant disclosure page 20) which is also part of the abstract idea due to a Markov model and Hidden Markov model being statistical models, and the bidirectional mapping links abstract data to the abstract pathological model (see instant disclosure page 19). Further, personalized disease state prediction and prediction of treatment efficacy and/or need and disease state modeling and treatment fall under the abstract idea itself. Therefore, the argued improvement falls under the abstract idea itself and constitutes as an improvement to the abstract idea and not an improvement to technology (due to the improvement not being provided by the additional element or the combination of the additional element and the judicial exceptions).
Argument 2:
Applicant argues that the Federal Circuit has recognized that claims improving computer performance and functionality are not abstract (e.g., SRIInt'l v. Cisco, 930 F.3d 1295 (Fed. Cir. 2019); Ancora Techs. v. HTC, 908 F.3d 1343 (Fed. Cir. 2018)). Like those cases, the present claims recite a specific architecture and set of data structures that improve machine learning-based prediction at the machine level (Reply p. 15).
This argument has been fully considered but found to be not persuasive. The MPEP states at 2106.05(a) “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements… In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception”. The argued improvement in the personalized disease state prediction, prediction of treatment efficacy and/or need, disease state modeling, and treatment prediction. Therefore, this improvement is realized and provided in the abstract idea of processing abstract data to provide an improved prediction. The argued improvement is not provided by or realized in the additional elements of the computer system itself or the computer network itself. The computer itself does not function in a manner which is different or improved, the computer is utilized as a tool to process abstract data with an improved abstract data analysis.
Argument 3:
Applicant argues that Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016) holds that claim reciting a "specific improvement to the way computers operate" are not directed to an abstract idea. Under Enfish, these claim elements define a particular data structure(s) and set of operations that improve the functioning of machine learning modeling, rather than merely using a computer as a tool. The focus is on how the computer/network itself operates (bidirectional mapping data structure, individualized transition matrix, refitting, etc.), so the claims are not directed to an abstract idea (Reply p. 15).
This argument has been fully considered but found to be not persuasive. Enfish has a different fact pattern than the instant case. The claims in Enfish were found to be directed to a specific type of data structure designed to improve the way a computer stores and retrieves data in memory. The claims in Enfish provide an improvement which is realized in the additional elements of the computer memory and computer system be improving the computer operation of storing and retrieving data. In contrast, the argued improvement in the instant case is directed to how abstract data is analyzed to provide an improved personalized disease state prediction, prediction of treatment efficacy and/or need, disease state modeling, and treatment prediction. Further, the bidirectional mapping data structure, the individualized transition matrix, and refitting do not interact with the computer system/network in a manner which improves computer functionality itself, rather the abstract data structures improve the abstract idea of processing data using models to provide an improved personalized disease state prediction or improved prediction of treatment efficacy and/or need. Therefore, the improvement is provided by and realized in the judicial exceptions of processing abstract data which does not constitute as an improvement to a technology (see MPEP 2106.05(a)).
Argument 4:
Applicant argues the claims utilize machine learning innovations to provide a specific data scheme to enable new functionality. As in Ex Parte Desjardins, the present claims are more than mere generic computer components with an unpatentable algorithm but rather provide a particular way of improving the operation of the machine learning through a particularized application of data structures and training techniques (Reply p. 15-16).
This argument has been fully considered but found to be not persuasive. The instant claims have a different fact pattern than the claims in Ex Parte Desjardins. The claims in Ex Parte Desjardins were found to improve machine learning models themselves through a particular training process which solved the technical problem of “catastrophic forgetting” in multi-task learning in neural networks. In contrast, the argued improvement in the instant claims is an improvement in personalized disease state prediction, prediction of treatment efficacy and/or need, disease state modeling and treatment prediction which are predictions made through abstract data analysis utilizing models. The claims do not provide an improvement to a machine learning model itself, the argued improvement provides an improved prediction of disease states and treatment efficacy which are abstract ideas of data analysis by utilizing models. Thus, the argued improvement falls under the abstract idea itself which cannot provide the improvement.
Argument 5:
Applicant argues that here as in McRO, the claims provide specific steps that renders information into a specific format (e.g., the bidirectional mapping and transition matrix) to create a desired result (personalized disease state prediction from a data from a plurality of patients (Reply p. 16-17). Applicant further argues that the present claims provide a computer-based technological improvement that uses a combined order of specific rules that achieve the technological improvement (Reply p. 17).
This argument has been fully considered but found to be not persuasive. The instant claims have a different fact pattern than McRO. The claims in McRO were found to provide an improvement in allowing computers to produce “accurate and realistic lip synchronization and facial expressions in animated characters” that previously could only be produced by human animators. The claims in McRO provide an improvement which is realized in computer animation (i.e., the technology of computer animation) by allowing computers to perform a function that previously required human animators which is an improvement realized in the functionality of a computer. In contrast, the argued improvement in the instant case is directed to how abstract data is analyzed to provide an improved personalized disease state prediction, prediction of treatment efficacy and/or need, disease state modeling, and treatment prediction. It is noted that unlike computer animation, personalized disease state predictions and treatment efficacy predictions do not require a computer to perform. These predictions are abstract ideas which are a result of utilizing abstract ideas to process data. Thus, the argued improvement is not analogous to the improvement found in McRO and the instant claims provide an improvement in the abstract idea of data analysis.
Conclusion
No claims are allowed.
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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/J.E.H./Examiner, Art Unit 1685
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685