Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are pending in the present application with claims 1, 8, and 15 being independent.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “310” has been used to designate both “SCGM initialization component” and “Input data” in Figure 3. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: “Historical medical records 221.” Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
-In [0032], it appears that “few medical records 221” should be changed to --few medical records 277--.
-In [0042], it appears that “pooling” should be changed to --polling--.
Appropriate correction is required.
Claim Objections
Claims 1, 6, 8, 13, 15, and 20 are objected to because of the following informalities:
All occurrences of "DCNN" should be changed to --DCCN--.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 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.
Claims 1, 8, and 15 recite the limitations "the iterative optimization" and “the support data." There is insufficient antecedent basis for these limitation in the claims.
Claims 4, 11, and 18 recite the limitation “the test data.” There is insufficient antecedent basis for this limitation in the claims.
Furthermore, one of ordinary skill in the art would not understand how the iterative optimization relates to the performing of the forward computation or how the model personalization relates to the model convergence.
More specifically, what is being “iteratively optimized”?
How does one of ordinary skill in the art determine when such iterative optimization has “converged”?
Still further, the claims recite performing “model personalization” but never recite a model to personalize.
Are “the embeddings and event subtype labels” a result of “encoding the support data of a new patient”?
Are “the embeddings” a result of “encoding the tests data of a new dialysis patient”?
Regarding claims 6, 13, and 20, it is not clear if the “outputs of the static channel and the temporal channel” are the encoded static patient profiles and the encoded temporal patient status features.
The remaining claims are rejected based on their dependencies from one of the above claims
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3 and 5-16 of U.S. Patent No. 12,481,881 ("the '881 reference patent"). Although the claims at issue are not identical, they are not patentably distinct from each other:
For reference, a chart comparing independent claim 1 of the present invention to independent claim 1 of the '881 reference patent is presented below:
Independent Claim 1 of Present Application
Independent Claim 1 of the '881 reference patent
1. A computer-implemented method for model building, comprising:
receiving a training set of medical records and model hyperparameters;
initializing an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters;
performing, by a hardware processor, a forward computation to (1) the DCNN to obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities;
checking by a convergence evaluator if the iterative optimization has converged; and
performing model personalization responsive to model convergence by encoding the support data of a new patient and using the embeddings and event subtype labels to train a personalized classifier.
1. A computer-implemented method for model building, comprising:
receiving a training set of dialysis records and model hyperparameters;
initializing an encoder, as a Dual-Channel Combiner Network (DCCN), and distribution related parameters;
building a model using a forward computation to (1) the encoder to obtain embeddings of the dialysis records by using a multilayer perceptron (MLP) of the DCCN to encode static patient profiles of the dialysis records and one or more Long Short-Term Memories (LSTMs) of the DCCN to encode temporal patient status features of the medical records, and (2) the distribution related parameters to obtain membership probabilities of a plurality of classes;
manipulating the embeddings of the dialysis records with the distribution related parameters to enable adaptation of the model to a fine-grained multi-class task;
performing iterative optimization of the model between (1) a step for obtaining posterior probabilities indicative of membership probabilities of the embeddings in one or more subclasses of each class of the plurality of classes, and (2) a step for obtaining updated encoder and distribution related model parameters while fixing one or more of the obtained posterior probabilities; and
personalizing the model responsive to convergence of the iterative optimization with a final set of updated encoder and distribution related model parameters by encoding support data of a new dialysis patient to obtain new embeddings and using the new embeddings and event subtype labels including different unstable patterns of blood pressure to train a personalized classifier for the new dialysis patient that is configured to perform the fine-grained multi-class task.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, and 4-13 of U.S. Patent No. 12,481,882 ("the '882 reference patent"). Although the claims at issue are not identical, they are not patentably distinct from each other.
For reference, a chart comparing independent claim 1 of the present invention to independent claim 1 of the '882 reference patent is presented below:
Independent Claim 1 of Present Application
Independent Claim 1 of the '882 reference patent
1. A computer-implemented method for model building, comprising:
receiving a training set of medical records and model hyperparameters;
initializing an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters;
performing, by a hardware processor, a forward computation to (1) the DCNN to obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities;
checking by a convergence evaluator if the iterative optimization has converged; and
performing model personalization responsive to model convergence by encoding the support data of a new patient and using the embeddings and event subtype labels to train a personalized classifier.
1. A computer-implemented method for model building, comprising:
receiving a training set of medical records and model hyperparameters;
initializing an encoder, as a Dual-Channel Combiner Network (DCCN), and distribution related parameters;
building a model using a forward computation to (1) the encoder to obtain embeddings of the medical records by using a multilayer perceptron (MLP) of the DCCN to encode static patient profiles of the medical records and one or more Long Short-Term Memories (LSTMs) of the DCCN to encode temporal patient status features of the medical records, and (2) the distribution related parameters to obtain membership probabilities of a plurality of classes;
manipulating the embeddings of the medical records with the distribution related parameters to enable adaptation of the model to a fine-grained multi-class task;
performing iterative optimization of the model between (1) a step for obtaining posterior probabilities indicative of membership probabilities of the embeddings in one or more subclasses of each class of the plurality of classes, and (2) a step for obtaining updated encoder and distribution related model parameters while fixing one or more of the obtained posterior probabilities;
personalizing the model responsive to convergence of the iterative optimization with a final set of updated encoder and distribution related model parameters by encoding support data of a new patient to obtain new embeddings and using the new embeddings to train a personalized classifier for the new patient that is configured to perform the fine-grained multi-class task; and performing model testing by encoding test data of the new patient to obtain test embedding and using the test embeddings and the personalized classifier to predict event subtypes.
Claims 1-3, 5-10, 12-17, 19, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, and 4-13 of U.S. Patent No. 12,481,883 ("the '883 reference patent"). Although the claims at issue are not identical, they are not patentably distinct from each other.
For reference, a chart comparing independent claim 1 of the present invention to independent claim 1 of the '883 reference patent is presented below:
Independent Claim 1 of Present Application
Independent Claim 1 of the '883 reference patent
1. A computer-implemented method for model building, comprising:
receiving a training set of medical records and model hyperparameters;
initializing an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters;
performing, by a hardware processor, a forward computation to (1) the DCNN to obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities;
checking by a convergence evaluator if the iterative optimization has converged; and
performing model personalization responsive to model convergence by encoding the support data of a new patient and using the embeddings and event subtype labels to train a personalized classifier.
1. A computer-implemented method for model building, comprising:
receiving a training set of medical records and model hyperparameters;
initializing an encoder, as a Dual-Channel Combiner Network (DCCN), and distribution related parameters;
building a model using a forward computation to (1) the encoder to obtain embeddings of the medical records by using a multilayer perceptron (MLP) of the DCCN to encode static patient profiles of the medical records and one or more Long Short-Term Memories (LSTMs) of the DCCN to encode temporal patient status features of the medical records, and (2) the distribution related parameters to obtain membership probabilities of a plurality of classes;
manipulating the embeddings of the medical records with the distribution related parameters to enable adaptation of the model to a fine-grained multi-class task;
performing iterative optimization of the model between (1) a step for obtaining posterior probabilities indicative of membership probabilities of the embeddings in one or more subclasses of each class of the plurality of classes, and (2) a step for obtaining updated encoder and distribution related model parameters while fixing one or more of the obtained posterior probabilities; and
personalizing the model responsive to convergence of the iterative optimization with a final set of updated encoder and distribution related model parameters by encoding support data of a new patient to obtain new embeddings and using the new embeddings to train a personalized classifier for the new patient that is configured to perform the fine-grained multi-class task.
Claims 4, 11, and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2 and 4-13 of U.S. Patent No. 12,481,883 ("the '883 reference patent") in view of U.S. Patent App. Pub. No. 2022/0104737 to Smit et al. ("Smit"). Although the claims at issue are not identical, they are not patentably distinct from each other.
Regarding claim 4, claim 1 of the '883 reference patent discloses all the limitations of the computer-implemented method of claim 1 but is silent regarding performing model testing by encoding the test data of the new patient and using the embeddings and the personalized classifier to predict event subtypes.
Nevertheless, Smit teaches that it was known in the machine learning and healthcare informatics art to test a trained model on a set of test data not yet encountered by model to determine how closely oxygen saturation levels predicted by the model match the expected target oxygen saturation levels of the test data and evaluate and further refine the model based on the analysis.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have performed model testing by testing the model on test data of a new patient which, in the case of the model of claim 1 of the '883 Patent, would involve encoding the new patient test data and using the embeddings and the personalized classifier to predict event subtypes similar to as taught by Smit to advantageously determine how closely predictions of the model match expectations of the test data thereby allowing for evaluation and further refinement the model based on the analysis. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claims 11 and 18 are rejected in view of the combination of claim 1 of the '883 reference patent and Smit as discussed above in relation to claim 4.
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 8-14 are rejected under 35 U.S.C. 101 because the claimed invention does not fall within at least one of the four categories of patent eligible subject matter:
Regarding claims 8-14 which are directed to “A computer program product...comprising a non-transitory computer readable storage medium,” the specification never appears to state that a “computer program product” does not include transitory signals, thus leaving open the possibility that the recited computer program product also includes transitory propagating signals per se (due to the open-ended nature of the term "comprising").
When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. $101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. §101, Aug. 24, 2009; p. 2.
As the scope of claims 8-14 includes transitory signals which are not within one of the four statutory categories of 35 U.S.C. §101, claims 8-14 are rejected under 35 U.S.C. §101.
It is recommended that Applicant amends these claims to recite --A non-transitory computer program product…-- in the next Response.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more:
Subject Matter Eligibility Criteria - Step 1:
Claims 1-7 are directed to a method (i.e., a process) and claims 15-20 are directed to a system (i.e., a machine). Accordingly, claims 1-7 and 15-20 are all within at least one of the four statutory categories. 35 USC §101. While claims 8-14 are not within one of the four statutory categories as noted above, they will be analyzed below in the interest of completeness.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 and July 2024 updates issued by the USPTO as incorporated into the MPEP, as supported by relevant case law), 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).
Representative independent claim 15 includes limitations that recite at least one abstract idea. Specifically, independent claim 15 recites:
A computer processing system for model building, comprising:
a memory device for storing program code; and
a hardware processor operatively coupled to the memory device for storing program code to
receive a training set of medical records and model hyperparameters;
initialize an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters;
perform, by a hardware processor, a forward computation to
(1) the DCNN to obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities;
check if the iterative optimization has converged; and
perform model personalization responsive to model convergence by encoding the support data of a new dialysis patient and using the embeddings and event subtype labels to train a personalized classifier.
The Examiner submits that many of the foregoing underlined limitations constitute “mental processes” because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). As an example, a person could practically in their mind with pen and paper perform a “forward computation” to obtain embeddings of medical records. For instance, a medical professional could readily review dialysis records of patients (e.g., that include various static variables such as demographics; temporal variables such as blood pressure, BG levels, etc.; dialysis parameters such as flow rate, etc.) and develop vector embeddings that include numerical representations of the dialysis records. The person could also practically in their mind determine whether “iterative optimization” has converged (e.g., whether a loss/error between predicted and known outputs is less than a threshold) and encode support data of a new dialysis patient (e.g., via developing vector embeddings that include numerical representations of the support data). These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis which amounted to "mental processes" in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
Furthermore, performing a forward computation to the distribution related parameters to obtain class probabilities encompasses mathematical calculations (“mathematical concepts”) as evidenced by at least [0051]-[0052] and the associated equations of the present specification.
Still further, checking if the iterative optimization has converged encompasses mathematical calculations (“mathematical concepts”) at least as evidenced by [0066]-[0072] and the associated equations of the present specification.
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 2-4, 6, 9-11, 13, 16-18, and 20 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below:
-Claims 2, 9, and 16 recite how performing a forward computation includes computing a loss function with the embeddings and the class probabilities for optimization which relates to mathematical calculations (“mathematical concepts”) as evidenced by at least [0072] and the associated equations of the present specification.
-Claims 3, 10, and 17 recite how performing the forward computation includes performing alternate optimization for a predefined numbers of iterations between (1) a step for obtaining posterior probabilities, and (2) a step for obtaining updated model parameters which relates to mathematical calculations (“mathematical concepts”) as evidenced by at least [0073]-[0077] and the associated equations of the present specification.
-Claims 4, 11, and 18 call for encoding the test data of the new dialysis patient and using the embeddings to predict event subtypes which is practically performable in the human mind with pen and paper ("mental processes"). For instance, in the case that the vector embeddings of the test data are similar (e.g., within any appropriate similarity range or the like) to certain vector embeddings of the support data that are associated with particular event subtype labels, the medical professional could predict that the test data of the new patient would be associated with the same particular event subtype labels.
-Claims 6, 13, and 20 call for encoding static patient profiles and temporal patient status features, concatenating the outputs of the channels, and projecting the concatenation for prediction which can be practically performed in the human mind with pen and paper ("mental processes"). For instance, a person could practically in their mind with pen and paper develop vector embeddings that include numerical representations of the static and temporal features and then combine/project the embeddings/outputs into a “compact embedding” (e.g., a matrix of the numerical representations) that can be used for prediction of event subtypes.
Subject Matter Eligibility Criteria - Alice/Mayo Test: 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 abstract idea into a practical application. As noted at MPEP §2106.04(II)(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 such as 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 do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A computer processing system for model building (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), comprising:
a memory device for storing program code (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)); and
a hardware processor operatively coupled to the memory device for storing program code to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f))
receive a training set of medical records and model hyperparameters (extra-solution activity (data gathering) as noted below, see MPEP § 2106.05(g));
initialize an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f));
perform, by a hardware processor (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), a forward computation to
(1) the DCNN to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities;
check by a convergence evaluator (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) if the iterative optimization has converged; and
perform model personalization responsive to model convergence by (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)) encoding the support data of a new dialysis patient and using the embeddings and event subtype labels to train a personalized classifier (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)).
For the following reasons, the Examiner submits that the above-identified additional limitations, when considered as a whole with the limitations reciting the at least one abstract idea, do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the computer processing system including memory device and hardware processor, the encoder, and the convergence evaluator, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitation of receiving the training set, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)).
Regarding the additional limitations of initializing the encoder as the DCNN, initializing distribution related parameters, performing model personalization responsive to model convergence using the embedding and event subtype labels to train a personalized classifier, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the initialization and model personalization actually occur.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Furthermore, looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 15 and analogous independent claims 1 and 8 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 15 and analogous independent claims 1 and 8 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
-Claims 4, 11, and 18 call for performing model testing using the personalized classifier to predict the subtypes which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished and thus is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the model testing actually occurs.
-Claims 5, 12, and 19 call for performing a dialysis event on a patient responsive to the predicted event subtypes which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
-Claims 6, 13, and 20 recite how the encoder includes static and temporal channels which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
-Claims 7 and 14 recites how the static channel comprises a multilayer perceptron (MLP), and wherein the temporal channel comprises one or more Long Short-Term Memories (LSTMs) which again does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
When the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 15 does 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.
Regarding the additional limitations of the computer processing system including memory device and hardware processor, the encoder, and the convergence evaluator, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of initializing the encoder as the DCNN, initializing distribution related parameters, performing model personalization responsive to model convergence using the embedding and event subtype labels to train a personalized classifier, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the initialization and model personalization actually occur.
Regarding the additional limitations directed to receiving the training set which the Examiner submits merely adds insignificant extra-solution activity to the abstract idea (see MPEP § 2106.05(g)), the Examiner has reevaluated such limitation and determined it to not be unconventional as it merely consists of receiving/transmitting data over a network. See Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016); See MPEP 2106.05(d)(II).
The dependent claims also 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 the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
-Claims 4, 11, and 18 call for performing model testing using the personalized classifier to predict the subtypes which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished and thus is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the model testing actually occurs.
-Claims 5, 12, and 19 call for performing a dialysis event on a patient responsive to the predicted event subtypes which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
-Claims 6, 13, and 20 recite how the encoder includes static and temporal channels which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
-Claims 7 and 14 recites how the static channel comprises a multilayer perceptron (MLP), and wherein the temporal channel comprises one or more Long Short-Term Memories (LSTMs) which again does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
Therefore, claims 1-20 are ineligible under 35 USC §101 for being directed to an abstract idea without significantly more.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892. For instance, NPL "Predicting Clinical Events by Combining Static and Dynamic Information using Recurrent Neural Networks" to Esteban et al. discloses how static information (e.g. patient gender, blood type, etc.) combined with sequences of data that are recorded during multiple hospital visits (e.g. medications prescribed, tests performed, etc.) are often found in clinical data sets. In this work, an approach based on RNNs, specifically designed for the clinical domain, that combines static and dynamic information in order to predict future events is presented. The goal is to predict, based on information recorded in the Electronic Health Record of each patient, whether any of a plurality of different endpoints will occur within the next six or twelve months after each visit to the clinic. Different types of RNNs are compared, with a model based on a Feedforward Neural Network and a Logistic Regression model. The RNN developed based on Gated Recurrent Units provides the best performance for this task. The same models are used for a second task, i.e., next event prediction, and found that here the model based on a Feedforward Neural Network outperformed the other models.
Also for instance, NPL "Prediction of Clinical Events in Hemodialysis Patients Using an Artificial Neural Network" to Putra et al. discloses use of an ANN to predict clinical events during hemodialysis (HD) sessions. Vital signs, captured using a non-contact bed-sensor, and demographic information from the electronic medical records for 109 patients enrolled in the study are used. Weka Workbench software (open-source ML software) was used to train and validate the ANN model. The prediction model was built using a Multilayer perceptron (MLP) algorithm as part of the ANN with 10-fold cross-validation. The model showed mean precision and recall of 93.45% and AUC of 96.7%. Age was the most important variable for static feature and heart rate for dynamic feature. This model can be used to predict the risk of clinical events among HD patients and can support decision-making for healthcare professionals.
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686