CTNF 18/033,393 CTNF 81957 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION 1. Claims 1-20 are presented for examination. Claim Objections 07-29-01 AIA 2. Claim 20 is objected to because of the following informalities: As per claim 20, it recites “The computer readable medium of claim 1,”, but the claim 1 is a method claim . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 3. Claims 3, 6-7 and 14 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. As per Claim 3 and 14, they recite the limitation “substantially the same as” which is a relative term which renders the claim indefinite. The term “substantially” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. As per Claim 6-7, they recite the limitation “the initial notification time”. There is insufficient antecedent basis for this limitation in the claim. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. 4. Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception, an abstract idea, as it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. (Step 1) The claim 1-11 is directed methods and fall within the statutory category of processes; and, the claim 12-18 is directed to an apparatus and falls within the statutory category of machines. The claim 19-20 is directed to a non-transitory computer readable medium which is one of the statutory categories of invention. (Step 2A – Prong One) For the sake of identifying the abstract ideas, a copy of the claim is provided below. Abstract ideas are bolded. Claim 1, 12, and 19 recite: “ training a predictive algorithm for predicting an adverse medical event at a desired notification time before occurrence of the adverse medical event using initial annotations indicating times for diagnosis of the adverse medical event (insignificant extra-solution activity – data gathering and/or field of use) ; determining real risk scores over time using the predictive algorithm applied to the subjects in the medical facility, and identifying a corresponding trend of the real risk scores ( under its broadest reasonable interpretation, a mathematical concept and a mental process that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion) , wherein the real risk scores indicate actual probabilities of the adverse medical event occurring at predetermined times before the occurrence of the adverse medical event (insignificant extra-solution activity – data gathering and/or field of use) ; creating required risk scores over time based on the real risk score trend ( under its broadest reasonable interpretation, a mathematical concept and a mental process that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion) , wherein the required risk scores indicate modified probabilities of the adverse medical event occurring at the predetermined times before the occurrence of the adverse medical event (insignificant extra-solution activity – data gathering and/or field of use) ; mapping the initial annotations to a time-series of new annotations that minimizes differences between the required risk scores and the real risk scores ( under its broadest reasonable interpretation, a mathematical concept and a mental process that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion) ; fine-tuning the predictive algorithm for predicting the adverse medical event at the desired notification time using the time-series of new annotations ( under its broadest reasonable interpretation, a mathematical concept and a mental process that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion) ; and monitoring at least one of the subjects in the medical facility by applying the fine-tuned predictive algorithm to indicate the desired notification time ( insignificant extra-solution activity - “apply it” and/or field of use) . Therefore, the limitations, under the broadest reasonable interpretation, have been identified to recite judicial exceptions, an abstract idea. (Step 2A – Prong Two: integration into practical application) This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements of “A system… an interface… a processor… a memory” (Claim 12) and “a display” (Claim 16) which is recited at high level generality and recited so generally that they represent more than mere instruction to apply the judicial exception on a computer (see MPEP 2106.05(f)). The limitation can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(d)). Further the additional limitation “the predictive algorithm” and “recurrent neural network (RNN)-based model” is an insignificant extra-solution activity which is generally linking the use of a judicial exception to a particular technological environment or field of use. Further Claims recite the limitation which is an insignificant extra-solution activity because it is a mere nominal or tangential addition to the claim, amounts to mere data gathering (see MPEP 2106.05(g)): (Claim 1, 12 and 19) “training a predictive algorithm for predicting an adverse medical event at a desired notification time before occurrence of the adverse medical event using initial annotations indicating times for diagnosis of the adverse medical event” (insignificant extra-solution activity – data gathering and/or field of use) ; (Claim 12) “receiving initial annotations indicating times for diagnosis of an adverse medical event in subjects of the medical facility” (insignificant extra-solution activity – data gathering and/or field of use). Further the claims recite the limitation which insignificant extra-solution activity for the act of outputting itself , is equivalent to “apply it”, and/or generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)): (Claim 1, 12, and 19) “monitoring at least one of the subjects in the medical facility by applying the fine-tuned predictive algorithm to indicate the desired notification time ( insignificant extra-solution activity - “apply it” and/or field of use) ”. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. (Step 2B - inventive concept) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “A system… an interface… a processor… a memory” (Claim 12) and “a display” (Claim 16) which is recited at high level generality and recited so generally that they represent more than mere instruction to apply the judicial exception on a computer (see MPEP 2106.05(f)). The limitation can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(d)). Further the additional limitation “the predictive algorithm” and “recurrent neural network (RNN)-based model” is an insignificant extra-solution activity which is generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Further Claims recite the limitation which is an insignificant extra-solution activity because it is a mere nominal or tangential addition to the claim, amounts to mere data gathering (see MPEP 2106.05(g)) which is the element that the courts have recognized as well-understood, routine, conventional activity (see MPEP 2106.05(d) II. i. 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93): (Claim 1, 12 and 19) “training a predictive algorithm for predicting an adverse medical event at a desired notification time before occurrence of the adverse medical event using initial annotations indicating times for diagnosis of the adverse medical event” (insignificant extra-solution activity – data gathering and/or field of use) ; (Claim 12) “receiving initial annotations indicating times for diagnosis of an adverse medical event in subjects of the medical facility” (insignificant extra-solution activity – data gathering and/or field of use). Also the claims recite the limitation which insignificant extra-solution activity for the act of outputting itself , is equivalent to “apply it”, and/or generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)): (Claim 1, 12, and 19) “monitoring at least one of the subjects in the medical facility by applying the fine-tuned predictive algorithm to indicate the desired notification time ( insignificant extra-solution activity - “apply it” and/or field of use) ”. Further dependent claims 2-11, 13-18, and 20 recite: 2. The method of claim 1, wherein mapping the initial annotations to the time-series of new annotations comprises weighting each of the real risk scores by the required risk scores, respectively (a mathematical concept and a mental process) . 3. The method of claim 1, wherein a trend of the required risk scores over time has a curve that is substantially the same as a curve of the real risk score trend ( insignificant extra-solution activity – data gathering ). 4. The method of claim 1, further comprising: weighting the new annotations and determining a cut-off of the required risk scores in order to prevent early prediction of the adverse medical event before the desired notification time (a mathematical concept and a mental process) . 5. The method of claim 1, further comprising adjusting patient management based on the fine-tuned predictive algorithm and/or the monitoring ( insignificant extra-solution activity - “apply it” and/or field of use) . 6. The method of claim 5, wherein adjusting the patient management comprises adjusting staff and/or resource availability to compensate for the desired notification time being greater or less than the initial notification time ( a mental process) . 7. The method of claim 5, wherein adjusting the patient management comprises adjusting staff to compensate for a number of patients the desired notification time being greater or less than the initial notification time ( a mental process ). 8. The method of claim 1, further comprising adjusting a policy of the medical facility based on the fine-tuned predictive algorithm and/or the monitoring ( a mental process ). 9. The method of claim 1, wherein the predictive algorithm comprises a recurrent neural network (RNN)-based model ( insignificant extra-solution activity – generally linking the use of a judicial exception to a particular technological environment or field of use ). 10. The method of claim 1, wherein the adverse medical event comprises one of hemodynamics instability (HI), atrial fibrillation, acute kidney injury (AKI), pressure injury (PI), respiratory distress, acute lung injury or risk of infection ( insignificant extra-solution activity – data gathering and/or field of use ). 11. The method of claim 1, further comprising: providing a real cut-off indicating a predetermined value of the real risk scores ( insignificant extra-solution activity – data gathering and/or field of use ); optimizing the real cut-off to provide a required cut-off indicating a value of the required risk scores (a mathematical concept and a mental process) ; and preventing an alert or notification of the adverse medical event at values of the required risk scores below the required cut-off ( insignificant extra-solution activity – data outputting ). 13. The system of claim 12, wherein mapping the initial annotations to the time-series of new annotations comprises weighting each of the real risk scores by the required risk scores, respectively (a mathematical concept and a mental process) . 14. The system of claim 12, wherein a trend of the required risk scores over time has a curve that is substantially the same as a curve of the real risk score trend ( insignificant extra-solution activity – data gathering ). 15. The system of claim 12, wherein the instructions further cause the processor to perform weighting of the new annotations and determining a cut-off of the required risk scores in order to prevent early prediction of the adverse medical event before the desired notification time (a mathematical concept and a mental process) . 16. The system of claim 12, further comprising: a display for displaying the desired notification time and/or a graph for tracking the desired notification time with respect to the subject ( insignificant extra-solution activity – data outputting ). 17. The system of claim 16, wherein patient management is adjusted based on at least one of the displayed graph, the fine-tuned predictive algorithm or the monitoring ( insignificant extra-solution activity - “apply it” and/or field of use) . 18. The system of claim 12, wherein the predictive algorithm comprises a recurrent neural network (RNN)-based model ( insignificant extra-solution activity – generally linking the use of a judicial exception to a particular technological environment or field of use ). 20. The computer readable medium of claim 1, wherein mapping the initial annotations to the time-series of new annotations comprises weighting each of the real risk scores by the required risk scores, respectively (a mathematical concept and a mental process) . Considering the claim both individually and in combination, there is no element or combination of elements recited contains any “inventive concept” or adds “significantly more” to transform the abstract concept into a patent-eligible application. Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA 5. Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tomasev (US 11302446 B2) in view of Tomasev 2019 (“A clinically applicable approach to continuous prediction of future acute kidney injury”) and further in view of Hinton (“Distilling the Knowledge in a Neural Network”) . As per Claim 1, 12, and 19, Tomasev '446 teaches a method/system/non transitory medium of developing adaptable predictive analytics for subjects in a medical facility (Tomasev '446: Fig. 3, col. 1 lines 33-49 “ This specification describes a system that makes predictions that characterize the likelihood that a specific adverse health event will occur to a patient in the future” ), the method comprising: (Claim 12) an interface for receiving initial annotations indicating times for diagnosis of an adverse medical event in subjects of the medical facility; a processor in communication with the interface; and a memory that stores instructions that, when executed by the processor, causes the processor (Tomasev '446: col. 2, lines 45–60: " there is provided a system comprising one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising receiving electronic health record data for a patient "; col. 9, lines18-24: " provide the user interface presentation for display on the user computer. "). to perform a method comprising: (Claim 1, 12, and 19) training a predictive algorithm for predicting an adverse medical event at a desired notification time before occurrence of the adverse medical event using initial annotations indicating times for diagnosis of the adverse medical event (Tomasev '446: col. 1 lines 33-49 “ This specification describes a system that makes predictions that characterize the likelihood that a specific adverse health event will occur to a patient in the future. The predictions are made based on electronic health record data for the patient. In particular, the system generates, from the electronic health record data, an input sequence that includes a respective feature representation at each of a plurality of time window time steps and processes the input sequence using a neural network to generate a neural network output.”; col. 12 lines 33-67). Tomasev '446 fails to teach explicitly determining real risk scores over time using the predictive algorithm applied to the subjects in the medical facility, and identifying a corresponding trend of the real risk scores, wherein the real risk scores indicate actual probabilities of the adverse medical event occurring at predetermined times before the occurrence of the adverse medical event; creating required risk scores over time based on the real risk score trend, wherein the required risk scores indicate modified probabilities of the adverse medical event occurring at the predetermined times before the occurrence of the adverse medical event; mapping the initial annotations to a time-series of new annotations that minimizes differences between the required risk scores and the real risk scores; fine-tuning the predictive algorithm for predicting the adverse medical event at the desired notification time using the time-series of new annotations; and monitoring at least one of the subjects in the medical facility by applying the fine-tuned predictive algorithm to indicate the desired notification time. Tomasev 2019 teaches determining real risk scores over time using the predictive algorithm applied to the subjects in the medical facility, and identifying a corresponding trend of the real risk scores, wherein the real risk scores indicate actual probabilities of the adverse medical event occurring at predetermined times before the occurrence of the adverse medical event (Tomasev 2019 p. 116 right column: " the model outputs a probability of AKI occurring at any stage of severity within the next 48 h (although our approach can be extended to other time windows or severities of AKI; see Extended Data Table 1)… At every point throughout an admission, the model provides updated estimates of future AKI risk along with an associated degree of uncertainty "; p. 121 left column, Methods: " PNG media_image1.png 182 613 media_image1.png Greyscale "); … a time-series of new annotations that minimizes differences between the required risk scores and the real risk scores (p. 121 right column, “ PNG media_image2.png 94 613 media_image2.png Greyscale : sparse diagnosis annotations converted to per-timestep labels via forward-fill”; p. 122 left column, “Each of the resulting eight outputs provides a binary prediction for AKI severity at a specific time window and is compared to the ground-truth label using the cross-entropy loss function (Bernoulli log-likelihood).”: cross-entropy loss between predicted and target distributions is the divergence-minimization operation); monitoring at least one of the subjects in the medical facility by applying the fine-tuned predictive algorithm to indicate the desired notification time (Figure 1, p. 116 right column, “ With our approach, 55.8% of inpatient AKI events of any severity were predicted early, within a window of up to 48 h in advance and with a ratio of 2 false predictions for every true positive… Identifying an increased risk of future AKI sufficiently far in advance is critical, as longer lead times may enable preventative action to be taken”) . In particular, Tomasev 2019 teaches that the RNN output is read as a continuous time-varying risk-score trajectory across multiple predetermined lead-time windows which converting sparse initial diagnosis annotations into a per-timestep time-series of ground-truth labels for training. Further Hinton teaches creating required risk scores over time based on the real risk score trend, wherein the required risk scores indicate modified probabilities of the adverse medical event occurring at the predetermined times before the occurrence of the adverse medical event (section Introduction “ An obvious way to transfer the generalization ability of the cumbersome model to a small model is to use the class probabilities produced by the cumbersome model as 'soft targets' for training the small model ”; section Distillation " Using a higher value for T produces a softer probability distribution over classes. ") ; mapping the initial annotations… (section Distillation : " In the simplest form of distillation, knowledge is transferred to the distilled model by training it on a transfer set and using a soft target distribution for each case in the transfer set that is produced by using the cumbersome model with a high temperature in its softmax ", " The first objective function is the cross entropy with the soft targets and this cross entropy is computed using the same high temperature in the softmax of the distilled model as was used for generating the soft targets from the cumbersome model ": soft-target retraining is structurally the annotation-remap mechanism); fine-tuning the predictive algorithm for predicting the adverse medical event at the desired notification time using the time-series of new annotations (section Distillation: " we could use the original training set. We have found that using the original training set works well, especially if we add a small term to the objective function that encourages the small model to predict the true targets as well as matching the soft targets provided by the cumbersome model "). In particular, Hinton teaches taking a trained model’s output distribution, applying temperature smoothing, and using the resulting modified probability distribution as a training target and fine-tuning the predictor on the time-series of soft-target labels by re-running training against the modified distribution Tomasev '446, Tomasev 2019 and Hinton are analogous art because they are both from the same field of endeavor, neural networks to model a system for predicting medical event. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the teaching of Tomasev 2019 and Hinton int the method for predicting future adverse health events using neural networks of Tomasev '446 in order to provide an improved model with confidence assessments for offering opportunities for identifying patients at risk within a time window that enables early treatment (Tomasev 2019: pg 116 left column) and to significantly improve the model which learn to distinguish fine-grained classes (Hinton: Abstract). As per Claim 2, 13, and 20, Tomasev '446 fails to teach explicitly wherein mapping the initial annotations to the time-series of new annotations comprises weighting each of the real risk scores by the required risk scores, respectively. Hinton teaches wherein mapping the initial annotations to the time-series of new annotations comprises weighting each of the real risk scores by the required risk scores, respectively (section Distillation “ The first objective function is the cross entropy with the soft targets and this cross entropy is computed using the same high temperature in the softmax of the distilled model as was used for generating the soft targets from the cumbersome model. ”: Cross-entropy of predicted distribution against soft targets is mathematically a weighting of each real probability by the corresponding required-target value). As per Claim 3 and 14, Tomasev '446 fails to teach explicitly wherein a trend of the required risk scores over time has a curve that is substantially the same as a curve of the real risk score trend. Hinton teaches wherein a trend of the required risk scores over time has a curve that is substantially the same as a curve of the real risk score trend (section Introduction “ When we are distilling the knowledge from a large model into a small one, however, we can train the small model to generalize in the same way as the large model ”; section Soft Targets as Regularizers “ the same model trained with soft targets is able to recover almost all the information in the full training set ”: soft-target-trained model recovers the full information of the source distribution). As per Claim 4 and 15, Tomasev '446 fails to teach explicitly further comprising: weighting the new annotations and determining a cut-off of the required risk scores in order to prevent early prediction of the adverse medical event before the desired notification time. Tomasev 2019 teaches determining a cut-off of the required risk scores in order to prevent early prediction of the adverse medical event before the desired notification time (pg 116 right column, “When the predicted probability exceeds a specified operating-point threshold, the prediction is considered positive.”) . Hinton teaches weighting the new annotations (p 2 “ we add a small term to the objective function that encourages the small model to predict the true targets as well as matching the soft targets provided by the cumbersome model”). As per Claim 5, Tomasev '446 fails to teach explicitly further comprising adjusting patient management based on the fine-tuned predictive algorithm and/or the monitoring. Tomasev 2019 teaches further comprising adjusting patient management based on the fine-tuned predictive algorithm and/or the monitoring (pg 116 left column, " For predictive alerts to be effective, they must empower clinicians to act before a major clinical decline has occurred "). As per Claim 6, Tomasev '446 fails to teach explicitly wherein adjusting the patient management comprises adjusting staff and/or resource availability to compensate for the desired notification time being greater or less than the initial notification time. Tomasev 2019 teaches wherein adjusting the patient management comprises adjusting staff and/or resource availability to compensate for the desired notification time being greater or less than the initial notification time (p. 117 left column " To respond to these alerts on a daily basis, clinicians would need to attend to approximately 0.8% of in-hospital patients (Extended Data Table 2). "). As per Claim 7, Tomasev '446 fails to teach explicitly wherein adjusting the patient management comprises adjusting staff to compensate for a number of patients the desired notification time being greater or less than the initial notification time. Tomasev 2019 teaches wherein adjusting the patient management comprises adjusting staff to compensate for a number of patients the desired notification time being greater or less than the initial notification time (p. 117 left column " To respond to these alerts on a daily basis, clinicians would need to attend to approximately 0.8% of in-hospital patients (Extended Data Table 2). "). As per Claim 8, Tomasev '446 fails to teach explicitly further comprising adjusting a policy of the medical facility based on the fine-tuned predictive algorithm and/or the monitoring. Tomasev 2019 teaches further comprising adjusting a policy of the medical facility based on the fine-tuned predictive algorithm and/or the monitoring (p. 117 left column " An operating point can be chosen to further increase the proportion of AKI that is predicted early or to reduce the percentage of false predictions at each step, according to clinical priority "; p. 121 left column " We use a stacked multiple-layer recurrent network with highway connections… We use the simple recurrent unit network as the RNN architecture, with tanh activations. "). As per Claim 9 and 18, Tomasev '446 wherein the predictive algorithm comprises a recurrent neural network (RNN)-based model (col. 10 lines 47-56). As per Claim 10, Tomasev '446 fails to teach explicitly wherein the adverse medical event comprises one of hemodynamics instability (HI), atrial fibrillation, acute kidney injury (AKI), pressure injury (PI), respiratory distress, acute lung injury or risk of infection. Tomasev 2019 teaches wherein the adverse medical event comprises one of hemodynamics instability (HI), atrial fibrillation, acute kidney injury (AKI), pressure injury (PI), respiratory distress, acute lung injury or risk of infection (Title “ acute kidney injury ”). As per Claim 11, Tomasev '446 fails to teach explicitly further comprising: providing a real cut-off indicating a predetermined value of the real risk scores; optimizing the real cut-off to provide a required cut-off indicating a value of the required risk scores; and preventing an alert or notification of the adverse medical event at values of the required risk scores below the required cut-off. Tomasev 2019 teaches providing a real cut-off indicating a predetermined value of the real risk scores (pg 116 right column, “When the predicted probability exceeds a specified operating-point threshold, the prediction is considered positive.”; p. 117 left column " An operating point can be chosen to further increase the proportion of AKI that is predicted early or to reduce the percentage of false predictions at each step, according to clinical priority " ) ; preventing an alert or notification of the adverse medical event at values of the required risk scores below the required cut-off (pg 116 right column, “When the predicted probability exceeds a specified operating-point threshold, the prediction is considered positive.”; p. 117 left column " An operating point can be chosen to further increase the proportion of AKI that is predicted early or to reduce the percentage of false predictions at each step, according to clinical priority " ) . Hinton teaches optimizing the real cut-off to provide a required cut-off indicating a value of the required risk scores (p 2 “ we add a small term to the objective function that encourages the small model to predict the true targets as well as matching the soft targets provided by the cumbersome model”) . As per Claim 16, Tomasev '446 fails to teach explicitly further comprising: a display for displaying the desired notification time and/or a graph for tracking the desired notification time with respect to the subject. Tomasev 2019 teaches further comprising: a display for displaying the desired notification time and/or a graph for tracking the desired notification time with respect to the subject (Fig. 1). As per Claim 17, Tomasev '446 fails to teach explicitly wherein patient management is adjusted based on at least one of the displayed graph, the fine-tuned predictive algorithm or the monitoring. Tomasev 2019 teaches wherein patient management is adjusted based on at least one of the displayed graph, the fine-tuned predictive algorithm or the monitoring (p 116 " predictive alerts… must empower clinicians to act before a major clinical decline has occurred ", “ Identifying an increased risk of future AKI sufficiently far in advance is critical, as longer lead times may enable preventative action to be taken ”, Fig. 1) . Conclusion 07-96 AIA 6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hyland, S.L., Faltys, M., Hüser, M. et al. Early prediction of circulatory failure in the intensive care unit using machine learning. Nat Med 26, 364–373 (2020). https://doi.org/10.1038/s41591-020-0789-4. Bowman (US 20160371453 A1) 7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EUNHEE KIM whose telephone number is (571)272-2164. The examiner can normally be reached Monday-Friday 9am-5pm ET. 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, Ryan Pitaro can be reached at (571)272-4071. 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. EUNHEE KIM Primary Examiner Art Unit 2188 /EUNHEE KIM/ Primary Examiner, Art Unit 2188 Application/Control Number: 18/033,393 Page 2 Art Unit: 2188
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