DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is made final.
This office action is in response to the amendments filed March 11, 2026.
Claims 1, 11, 15, 24 have been amended.
Response to Amendment
The amendments filed March 11, 2026 has been entered. Claims 1-27 remain pending in the application.
Response to Arguments
Regarding the Obviousness/103 Arguments on pages 10-11
Applicant’s arguments with respect to claim(s) 1-27 have been considered but are moot because present rejection relies on newly applied reference(s) for the specially challenged limitations. Specifically:
Applicant Argues
Claims 1, 11, 19, and 20 are rejected as obvious over U.S. Pub. No. 2019/0325995 ("Malone") in view of U.S. Pat. No. 11,182,691 ("Zhang") and "An Introduction to Information Retrieval" ("Manning"). Claims 2-4 and 21-25 are rejected as obvious over Malone in view of Zhang, Manning, and U.S. Pub. No. 2024/0250979 ("Ding"). Claims 5, 6, and 12-18 are rejected as obvious over Malone in view of Zhang, Manning, and U.S. Pub. No. 2024/00 12658 ("Lin"). Claims 7-10, 26, and 27 are rejected as obvious over Malone in view of Zhang, Manning, and U.S. Pub. No. 2022/01728 12 ("Lu"). These rejections are respectfully traversed.
Support is provided in at least [0138], [0139], and FIG. 7 of the present application, as filed. Malone, Zhang, and Manning, alone or in any combination, fail to teach or suggest the added features of amended claim 1.
The Office Action acknowledges that Malone and Zhang fail to teach generating input variables and raises Manning. While Manning discusses tokenization and TF-IDF weighting applied to text documents, Manning is silent with respect to unionizing condition-specific data objects using common identifiers to generate merged condition-specific data and assigning an alphanumeric code to each data object of the merged condition-specific data, as required by amended claim 1.
For at least these reasons, the Applicant submits that claim 1 defines over the cited art.
Remaining Claims
Independent claim 11 includes similar features, and is patentable for at least similar reasons, as claim 1. The remaining claims are patentable at least by virtue of their dependency upon a patentable independent claim.
Examiner Response
Arguments regarding the rejection over Malone, Zhang, and Manning have been considered. Applicant argues that Manning is silent regarding unionizing condition-specific data objects using common identifiers to generate merge conditions specific data and assigning an alpha numeric code to each data object of the merge condition specific data. The argument is moot in view of the new ground of rejection set forth Below. The presently applied rejection relies on Chen, not Manning come for the limitations directed to unionizing condition specific data objects and assigning alphanumeric medical codes to data objects of the merge condition specific data. Manning is a relied upon for the downstream tokenization, scalar value Conversion, and frequency filtering limitations. Accordingly, applicant’s arguments do not overcome the rejection as presently applied.
Response to Subject Matter Rejection/101 on pages 11-13
Applicant's arguments filed March 11, 2026 have been fully considered but they are not persuasive. Specifically,:
Applicant Argues
Claims 1-27 stand rejected under 35 U.S.C. § 101 as allegedly not being directed to statutory subject matter.
REPRESENTATIVE CLAIM 1
While the Applicant does not necessarily agree that claim 1 recites an abstract idea, the Applicant has amended claim 1 to clarify the practical application of the
invention. Amended claim 1 is directed to a specific, computer-implemented system for processing clinical data and training a machine learning model and recites concrete technical steps that ground the claim in a practical application well beyond any alleged abstract idea.
Specifically, amended claim 1 clarifies that the system generates input variables through a defined clinical data integration and preprocessing pipeline in which condition-specific data objects are unionized using common identifiers to produce merged condition-specific data, an alphanumeric code is assigned to each data object of the merged data, each code is tokenized, the tokenized codes are converted to scalar values, and frequency filtering is applied to emphasize values appearing frequently within a data object while de-emphasizing those appearing frequently across data objects. This pipeline is applied to structured clinical code data, not generic text, and produces domain-specific input variables that improve the quality and relevance of the data provided to the machine learning model. By integrating condition-specific clinical data objects through a common identifier before preprocessing, the pipeline ensures that the machine learning model receives a more complete and coherent representation of each patient episode, reducing noise and improving the model's ability to generate accurate and clinically meaningful output variables.
As recognized in the recent precedential decision Ex parte Desjardins1, improvements that enhance how a machine-learning model operates-such as reducing system complexity, improving efficiency, and maintaining model performance across tasks-constitute meaningful technical advancements. Further, the Director of the USPTO includes the following guidance to the PTAB and examining corps:
Categorically excluding Al innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many Al innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24.
Examiners and panels should not evaluate claims at such a high
level of generality.
Ex Parte Desjardins, 9.
Consistent with that guidance, the input variable generation pipeline of amended claim 1 improves the operation of the machine learning system itself by producing higher-quality, clinically relevant input variables through a domain-specific multi- step transformation, rather than merely implementing an abstract idea on a generic computer. For at least these reasons, the Applicant respectfully submits that claim 1 is directed to eligible subject matter. REMAINING CLAIMS
Independent claim 11 includes similar features, and is directed to eligible subject matter for at least similar reasons, as claim 1. The remaining claims ultimately depend from claims 1 or 11 and are directed to eligible subject matter for at least the same reasons as the respective independent claims.
Examiner Response
Applicant argues that amended claim 1 is directed to eligible subject matter because the claimer cites a clinical data integration and preprocessing pipeline that generates higher quality, a clinically relevant input variables for a machine learning model. However, the added limitations are directed to collect income organizing, code and, tokenizing, numerical representing, and frequency filtering information before providing the information to a machine learning model. These operations constitute data organization and mathematical/ data processing operations performed using generic computer components, and do not come up by themselves, improve the functioning of the computer or the machine learning model itself.
Applicants’ reliance on Ex parte Desjardins is not persuasive. IN Desjardins, The claims were found to integrate the judicial exception into a practical application because the claims reflected a specific improvement to how the machine learning model itself operated, including training the model to learn new tasks while protecting knowledge from the previous tasks and thereby reducing storage requirements in system complexity. In contrast, amended claim one does not recite a particular improvement to the internal operation, architecture, training objective, parameter updating, memory usage, or computational efficiency of the machine learning model itself. Rather claim one recites preparing clinical data by merging condition specific data objects, assigning codes, tokenized encodes, converting tokens to scalar values, and applying frequency filtering before providing the resulting variables to a train model.
While such preprocessing may improve the quality or relevance of data provided to the model, the claim does not recite a technological solution to a technological problem comparable to the model operation improvement at issue in Desjardins. Any alleged improvement in prediction quality or clinical relevance arises from the information content and organization of the input data, Rather than from an improvement to computer functionality or other technology. Accordingly, the claim remains directed to the abstract idea identified in the previous office action and does not integrate the abstract idea into a practical application.
The additional elements, considered individually and as an ordered combination, do not amount to significantly more than the judicial exception. The claimer cites generic memory hardware, generic processing hardware, and conventional data processing operations such as merging data come assigning codes, tokenizing, converting to numerical values, frequency filtering, and providing values to machine learning model. These elements merely implement the abstract data processing concept on generic computer components and do not add an inventive concept.
Response to Double Patent on page 13
Applicant's arguments filed March 11, 2026 have been fully considered but they are not persuasive. Specifically,:
Applicant Argues
Claims 1-4, 7, 8, and 11-27 stand rejected on the ground of nonstatutory obviousness-type double patenting over U.S. Pub. No. 2024/0256944 in view of "Care Episode Retrieval" ("Moen"). Claims 1-27 stand rejected on the ground of nonstatutory obviousness-type double patenting over U.S. Pub. No. 2024/0256987 in view of Moen.
While the Applicant does not necessarily agree with the double patenting rejections, the Applicant has amended independent claims 1 and 11. Therefore, the Applicant respectfully requests reconsideration and withdrawal of the double patenting rejections.
Examiner Response
Applicant merely asserts an independent claims one in 11 have been amended In a request withdraw the double patent rejections. Applicant has not identified how the amended limitations render the incident Claims patently distinguished from the claims relied upon in the double patenting rejections. The amendments that claims 1 and 11 have been considered; however, the claims remain not patently distinct for the reasons set forth and maintained revised provisional nonstatory double patenting rejection.
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.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
Step 1: Determining if the claim falls within a statutory category.
Step 2A: Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and Step 2A is a two prong inquiry. MPEP 2106.04(II)(A). Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2104.04(a)(2). The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 2B: If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106).
Claims 1-27 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-27 are directed to a system, comprising one or more processors (a machine). Therefore, Claims 1-6 are directed to a process, machine or manufacture or composition of matter.
Regarding claim 1
Step 2A Prong 1
Claim 1 recites the following mathematical process, that in each case under the broadest reasonable interpretation, involves mathematical relationships, formulas, calculations, or algorithms implemented using generic computer components (e.g., “memory hardware”, “processing hardware”, and “ machine learning model”) [see MPEP 2106.04(a)(2)(I)].
“applying an under-sampling technique to elements of the first bin to generate an updated first bin”(e.g., statistical algorithm to adjust data distributions)
“applying an over-sampling technique to elements of the second bin to generate an updated second bin” (e.g., statistical algorithm to adjust data distributions)
“unionizing condition-specific data objects using common identifiers to generate merged condition-specific data” ( e.g., data organization and mathematical/data processing operations, including combining data sets based on common identifiers)
“tokenizing each alphanumeric code” (e.g., segmentation algorithm by splitting text into tokens)
“performing frequency filtering to emphasize scalar values based on a frequency the scalar values appear in a set of data objects while de-emphasizing scalar values based on a frequency the scalar values appear in a group of sets of data objects” (e.g., this is determining how often something appears is different sets of data and data groups)
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “memory hardware”, “processing hardware”, and “ machine learning model”) [see MPEP 2106.04(a)(2)(III)].
“assigning an alphanumeric code to each data object of the merged condition-specific data”(e.g., a human can look at data and assign alphanumeric strings to that data)
“converting the tokenized codes to scalar values” (e.g., a human can assign values to a generated string)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “memory hardware”, “processing hardware”, and “ machine learning model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). The Examiner notes that this is used throughout the claim limitations, and is rejected thusly for each claim which recites the same language.
Regarding the “loading a training data set, the training data set including a first bin and a second bin” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, i.e., pre-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)). The examiner notes that specify that the data set includes a first and second bin is merely a description of how the data is partitioned.
Regarding the “training the machine learning model with the updated training data set,” limitation, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). In particular, it is merely inputting data into the models and could also be interpreted as mere data gathering 2106.05(g).
Regarding the “saving the filtered scalar values as input variables” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, i.e., pre-solution activity of data outputting (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Regarding the “providing the input variables to the trained machine learning model to generate output variables” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, i.e., post-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Regarding the “a subsequent set of input variables is generated subsequent to the input variables” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of recreating further data input for subsequent models, i.e., post-solution activity of data outputting (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Regarding the “the input variables are archived to a database” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data from the model’s output, i.e., post-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Regarding the “the subsequent set of input variables are archived to the database in response to the episode identifiers of the subsequent set of input variables matching the episode identifiers of the input variables” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data from a model and comparing it to other saved model outputs i.e., post-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “memory hardware”, “processing hardware”, and “ machine learning model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “loading a training data set, the training data set including a first bin and a second bin”, “saving the filtered scalar values as input variables”, “providing the input variables to the trained machine learning model to generate output variables”, “a subsequent set of input variables is generated subsequent to the input variables”, “the input variables are archived to a database”, “the input variables are archived to a database”, and “the subsequent set of input variables are archived to the database in response to the episode identifiers of the subsequent set of input variables matching the episode identifiers of the input variables” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, saving data from a model, comparing the saved data and archiving the model outputs i.e., pre and post-solution activity of data gathering, and data outputting. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “training the machine learning model with the updated training data set,” limitation, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). In particular, it is merely inputting data into the models and could also be interpreted as mere data gathering 2106.05(g).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 2
Step 2A Prong 1
Claim 2 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
In accordance with Step 2A, Prong 2, the claim does not include any additional elements and the judicial exception is not integrated into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 2
Step 2A Prong 1
Claim 2 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a “automatically determining optimal hyperparameters for the trained machine learning model”, and “configuring the trained machine learning model with the optimal hyperparameters” limitations, which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “automatically determining optimal hyperparameters for the trained machine learning model”, and “configuring the trained machine learning model with the optimal hyperparameters” limitations, which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 3
Step 2A Prong 1
Claim 3 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “loading a training data set, the training data set including a first bin and a second bin” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, i.e., pre-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Regarding the “configuring the trained machine learning model with the baseline hyperparameters,” limitation, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). In particular, it is merely inputting data into the models and could also be interpreted as mere data gathering 2106.05(g).
Regarding the “running the configured machine learning model to determine baseline metrics” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data from a model, i.e., post-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Regarding the “in response to the baseline metrics being above a threshold, saving the baseline hyperparameters as the optimal hyperparameters” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data from a model and comparing it to a threshold to be saved, i.e., post-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “loading baseline hyperparameters”, “running the configured machine learning model to determine baseline metrics”, and “in response to the baseline metrics being above a threshold, saving the baseline hyperparameters as the optimal hyperparameters” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, saving data from a model, comparing the saved data and archiving the model outputs i.e., pre and post-solution activity of data gathering, and data outputting. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “configuring the trained machine learning model with the baseline hyperparameters,” limitation, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). In particular, it is merely inputting data into the models and could also be interpreted as mere data gathering 2106.05(g).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 4
Step 2A Prong 1
Claim 4 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “adjusting the baseline hyperparameters” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data from the model, comparing it to the threshold and if it fails to meet the threshold it is then adjusted for further processing, i.e., post-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Regarding the “reconfiguring the trained machine learning model with the adjusted hyperparameters,” limitation, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). In particular, it is merely inputting data into the models and could also be interpreted as mere data gathering 2106.05(g).
Regarding the “running the reconfigured machine learning model to determine updated metrics” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data from a model, i.e., post-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Regarding the “in response to the updated metrics being more optimal than the baseline metrics, saving the updated metrics as the optimal hyperparameters” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data from a model and comparing it to a threshold to be saved, i.e., post-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “adjusting the baseline hyperparameters”, “running the reconfigured machine learning model to determine updated metrics”, and “in response to the updated metrics being more optimal than the baseline metrics, saving the updated metrics as the optimal hyperparameters” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, saving data from a model, comparing the saved data and archiving the model outputs i.e., pre and post-solution activity of data gathering, and data outputting. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “reconfiguring the trained machine learning model with the adjusted hyperparameters,” limitation, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). In particular, it is merely inputting data into the models and could also be interpreted as mere data gathering 2106.05(g).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 5
Step 2A Prong 1
Claim 5 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “loading a second trained machine learning model”, and “providing the input variables to the second trained machine learning model to generate second output variables” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of obtaining data for training a model, i.e., pre-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “loading a second trained machine learning model”, and “providing the input variables to the second trained machine learning model to generate second output variables” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, i.e., pre-solution activity of data gathering, and data outputting. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 6
Step 2A Prong 1
Claim 6 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “loading a third trained machine learning model”, and “providing the input variables to the third trained machine learning model to generate third output variables” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of obtaining data for training a model, i.e., pre-solution activity of data gathering (e.g., obtaining information for processing in a computer system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “loading a third trained machine learning model”, and “providing the input variables to the third trained machine learning model to generate third output variables” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, i.e., pre-solution activity of data gathering, and data outputting. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 7
Step 2A Prong 1
Claim 7 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “machine learning model includes a light gradient-boosting machine model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “machine learning model includes a light gradient-boosting machine model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 8
Step 2A Prong 1
Claim 8 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “machine learning model includes a mixed effects random forests model with a light gradient-boosting machine regressor” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “machine learning model includes a mixed effects random forests model with a light gradient-boosting machine regressor” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 9
Step 2A Prong 1
Claim 9 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein input variables include a third bin and a fourth bin” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein input variables include a third bin and a fourth bin” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 10
Step 2A Prong 1
Claim 10 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the input variables of the third bin are assigned to fixed effects and the input variables of the fourth bin are assigned to mixed effects” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the input variables of the third bin are assigned to fixed effects and the input variables of the fourth bin are assigned to mixed effects” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 11 = 1-4
Regarding claim 12
Step 2A Prong 1
Claim 12 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “the input variables include an identifier of an entity in a population”, “the output variables include a score for the entity indicated by the identifier”, and “the score indicates a likelihood of a feature of merit exceeding a threshold” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “the input variables include an identifier of an entity in a population”, “the output variables include a score for the entity indicated by the identifier”, and “the score indicates a likelihood of a feature of merit exceeding a threshold” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 13
Step 2A Prong 1
Claim 13 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “generating a plurality of scores for a plurality of entities in the population”, and “clustering the plurality of scores into a plurality of clusters” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “generating a plurality of scores for a plurality of entities in the population”, and “clustering the plurality of scores into a plurality of clusters” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 14
Step 2A Prong 1
Claim 14 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the plurality of clusters is three clusters” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the plurality of clusters is three clusters” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 15
Step 2A Prong 1
Claim 15 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “the plurality of clusters includes a particular cluster associated with a greatest risk”, and “the instructions include adapting a graphical user interface in response to the score being assigned to the particular cluster” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “the plurality of clusters includes a particular cluster associated with a greatest risk”, and “the instructions include adapting a graphical user interface in response to the score being assigned to the particular cluster” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 16
Step 2A Prong 1
Claim 16 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the score is a value between zero and one hundred inclusive” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the score is a value between zero and one hundred inclusive” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 17
Step 2A Prong 1
Claim 17 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “the population includes entities that consume services”, and “the feature of merit is a measure of service consumption of the entity” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “the population includes entities that consume services”, and “the feature of merit is a measure of service consumption of the entity” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 18
Step 2A Prong 1
Claim 18 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “the population includes entities that coordinate services”, and “the feature of merit is an amount of services advised by the entity” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “the population includes entities that coordinate services”, and “the feature of merit is an amount of services advised by the entity” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 19
Step 2A Prong 1
Claim 19 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “partitioning the training data set into a first bin and a second bin”, limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of transforming datasets for inputting into a model, i.e., pre-solution activity of data gathering (see MPEP 2106.05(g)).
Regarding the “applying the over-sampling technique to elements of the first bin to generate an updated first bin”, and “applying the under-sampling technique to elements of the second bin to generate the updated second bin” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “partitioning the training data set into a first bin and a second bin” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of transforming datasets for inputting into a model, i.e., pre-solution activity of data gathering. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “applying the over-sampling technique to elements of the first bin to generate an updated first bin”, and “applying the under-sampling technique to elements of the second bin to generate the updated second bin” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 20
Step 2A Prong 1
Claim 20 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “model learning apparatus”, and “processor”) [see MPEP 2106.04(a)(2)(III)].
“merging the updated first bin and the updated second bin to generate a merged data structure” (e.g., a human can take updated data files and merge them together)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “saving the merged data structure as the updated training set”, limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of transforming datasets and saving the newly created dataset for inputting into a model, i.e., post-solution activity of data gathering (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “saving the merged data structure as the updated training set” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of transforming datasets and saving the newly created dataset for inputting into a model, i.e., post-solution activity of data gathering. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 21
Step 2A Prong 1
Claim 21 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “configuring the trained machine learning model with the baseline hyperparameters” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of inputting a dataset into a model for training, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated (see MPEP 2106.05(g)).
Regarding the “running the trained machine learning model configured with the baseline hyperparameters to generate baseline metrics”, limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of running the model to produce desired output, i.e., post-solution activity of data outputting (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “configuring the trained machine learning model with the baseline hyperparameters”, and “running the trained machine learning model configured with the baseline hyperparameters to generate baseline metrics” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of inputting a dataset into a model for training and running the model to produce desired output, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated and post-solution activity of data outputting. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 22
Step 2A Prong 1
Claim 22 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “parsing the baseline metrics to determine whether the baseline metrics are above a threshold”, and “in response to determining that the baseline metrics are above the threshold, saving the baseline hyperparameters as the optimal hyperparameters” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “parsing the baseline metrics to determine whether the baseline metrics are above a threshold”, and “in response to determining that the baseline metrics are above the threshold, saving the baseline hyperparameters as the optimal hyperparameters” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 23
Step 2A Prong 1
Claim 23 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “adjusting the hyperparameters” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “adjusting the hyperparameters” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 24
Step 2A Prong 1
Claim 24 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “configuring the training machine learning model with the adjusted hyperparameters”, and “running the trained machine learning model configured with the adjusted hyperparameters to generate updated metrics” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “configuring the training machine learning model with the adjusted hyperparameters”, and “running the trained machine learning model configured with the adjusted hyperparameters to generate updated metrics” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 25
Step 2A Prong 1
Claim 25 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “parsing the updated metrics to determine whether the updated metrics are more optimal than the baseline metrics”, and “in response to determining that the updated metrics are more optimal than the baseline metrics, saving the adjusted hyperparameters as the optimal hyperparameters” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “parsing the updated metrics to determine whether the updated metrics are more optimal than the baseline metrics”, and “in response to determining that the updated metrics are more optimal than the baseline metrics, saving the adjusted hyperparameters as the optimal hyperparameters” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 26
Step 2A Prong 1
Claim 26 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the machine learning model is a mixed effects random forest (MERF) with a light gradient-boosting machine (LightGBM) regressor” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the machine learning model is a mixed effects random forest (MERF) with a light gradient-boosting machine (LightGBM) regressor” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 27
Step 2A Prong 1
Claim 27 inherits the same abstract idea as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the output variables include at least one of (i) cost estimations for treatment groups and (ii) cost estimations for treatments” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the output variables include at least one of (i) cost estimations for treatment groups and (ii) cost estimations for treatments” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 11, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Malone et al. (US 20190325995 A1, referred to as Malone), in view of Zhang (US 11182691 B1, referred to as Zhang), in view of Chen et al. (WO2019022779 A1, referred to as Chen), in view of Manning et al. (“An Introduction to Information Retrieval” , referred to as Manning ).
Regarding claim 1, Malone teaches, a system comprising:
memory hardware configured to store instructions; and
processing hardware configured to execute the instructions, wherein the instructions include ([0035-0037]: Describes that the methods use a computer system to preform stored instructions from computer hardware.):
training a machine learning model by ([0038]: Describes that the hardware is configured to train machine learning models in a pipeline.):
a subsequent set of input variables is generated subsequent to the input variables([0043-0097]: Describes that a caretaker episode is represented by multiple episode snapshots that are transferred at different times as new patient data becomes available. The snapshots share the same episode identifier but with more measurements and fields populated as information becomes available. This indicates the system first generates an initial set of episode input data and then generates subsequent sets of episode input data (later snapshots) for the same episode as additional information arrives.);
the input variables and the subsequent set of input variables include episode identifiers([0043-0097]: Describes that each set of patient input variables is stored as an episode snapshot, and every episode snapshot includes an explicit episode identifier field. Since multiple snapshots are generated at different times for the same episode, both the initial set of input variables and any subsequent set include the same episode identifier.);
the input variables are archived to a database ([0040] and [0092]: Describes that each snapshot is transformed by the embedding model into an embedding, which is then stored in an episode embedding database 15, and the underlying episode snapshots themselves are maintained in an episode database 14. The systems episode level feature representations (input variables derived from episode data) are archived to dedicated databases.); and
the subsequent set of input variables are archived to the database in response to the episode identifiers of the subsequent set of input variables matching the episode identifiers of the input variables([0043-0097]: Describes that initial input variables and subsequent ones share the same ID. These store the new (subsequent) set back into the database entry for that episode. The episode identifier is used as the lookup key used to update the episode snapshots and storing new embeddings corresponding to that episode.).
Although Malone teaches, a system; memory hardware configured to store instructions; processing hardware configured to execute the instructions, wherein the instructions include; training a machine learning model; a subsequent set of input variables is generated subsequent to the input variables; the input variables and the subsequent set of input variables include episode identifiers; the input variables are archived to a database; and the subsequent set of input variables are archived to the database in response to the episode identifiers of the subsequent set of input variables matching the episode identifiers of the input. It does not teach, loading a training data set, the training data set including a first bin and a second bin; applying an under-sampling technique to elements of the first bin to generate an updated first bin; applying an over-sampling technique to elements of the second bin to generate an updated second bin; generating an updated training data set by merging the updated first bin and the updated second bin; and training the machine learning model with the updated training data set.
Zhang teaches, loading a training data set, the training data set including a first bin and a second bin (Col 54 likes 37-59: Describes training data set as raw or unmodified imbalanced data set, indicating that the training data is split between different kinds, a majority and minority category corresponding to bins.),
applying an under-sampling technique to elements of the first bin to generate an updated first bin (Col 56, lines 10-33: Describes a majority category (first bin) where a sampling ration less than 100% is applied, corresponding to a under sampling technique by reducing the number of examples form that training data group.),
applying an over-sampling technique to elements of the second bin to generate an updated second bin (Col 56, lines 10-33: While all these individual ratios are less than or equal to 100%, it is still representing a class imbalance by reducing the majority much more aggressively than the minority, thereby increasing the relative representation of the minority categories in the sampled training set. This is the same purpose as over sampling, making the minority categories more prominent in the training data relative to the majority.),
generating an updated training data set by merging the updated first bin and the updated second bin (Col 56, liens 10-58: Describes applying the sampling techniques and creating the updated bins for both, then after that is performed the newly updated training set 4102 is then obtained and ready for training models.), and
training the machine learning model with the updated training data set (Col. 56, lines 34-58: Describes that after the sampling the respective bins to create an updated/sampled training set, the machine learning model is trained using those updated training sets.);
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Malones machine learning model architecture of patient episode data with Zhang’s sampling techniques. Doing so have enabled the pipeline to incorporate sampling techniques to enhance the workflow with predictable model improvements and robustness.
Although Malone in view of Zhang teaches, loading a training data set, the training data set including a first bin and a second bin; applying an under-sampling technique to elements of the first bin to generate an updated first bin; applying an over-sampling technique to elements of the second bin to generate an updated second bin; generating an updated training data set by merging the updated first bin and the updated second bin; and training the machine learning model with the updated training data set. They do not teach unionizing condition-specific data objects using common identifiers to generate merged condition-specific data; and assigning an alphanumeric code to each data object of the merged condition-specific data.
Chen teaches, unionizing condition-specific data objects using common identifiers to generate merged condition-specific data (Pages 15 Lines 29-36, Page 16 lines 1-7, and Page 17 lines 7-25: Describes raw electronic health record’s from a multimode of patients and different institutions, in different data formats, are converted into a standardized format and arranged in an ordered arrangement, wherein the raw records for a patient include encounter tables, lab tables, vital signs, medical notes, demographic data, diagnoses, etc. the converter converts the raw data into FHIR resources, and for each patient there is a “bundle” or set of such resources. ; Page 15 lines 29-36, and p. 16, lines 1-7: Describes a single data structure for aggregated EHRs based on FHIR to store data from each system using standardized resources and that the set of resources for a given patient are assembled in chronological order. Corresponding to unionizing condition specific data objects using common identifiers to generate merged condition specific data as it aggregates raw HER data form different institutions/data formats, including encounter tales, lab tables, diagnoses, vital signs, medical notes, and demographic data, converting the data into standardized FHIR resources, and assembling a bundle/set of resources for each patient.),
assigning an alphanumeric code to each data object of the merged condition-specific data (Page 17 lines 7-25: Describes generating input variables by unionizing condition specific data objects using common identifiers to generate merged condition specific data and assigning alphanumeric codes to data objects of the merged condition specific data, it aggregates EWHR data form different sources/formats into a standardized structure and uses medical diagnosis/procedure codes such as ICD/CPT/CCS codes.),
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Malone, in view of Zhang’s learning system with Chen’s standardized electronic record data. Doing so would have enabled the system to harmonize heterogenous data from different sources into a standardized machine readable structure so the data can be used to predict future events and summarize past events.
Although Malone in view of Zhang, in view of Chen teaches, unionizing condition-specific data objects using common identifiers to generate merged condition-specific data; and assigning an alphanumeric code to each data object of the merged condition-specific data. They does not teach tokenizing each alphanumeric code; converting the tokenized codes to scalar values; performing frequency filtering to emphasize scalar values based on a frequency the scalar values appear in a set of data objects while de-emphasizing scalar values based on a frequency the scalar values appear in a group of sets of data objects; saving the filtered scalar values as input variables, and providing the input variables to the trained machine learning model to generate output variables.
Manning teaches, tokenizing each alphanumeric code (2.1.1, and 2.2.1: Describes that a raw data item begins as a byte sequence stored on disk. The text processing is to decode the raw bytes into a linear sequence of characters, corresponding to an alphanumeric string, this decoding assigns a character string representation to each element of the raw data set so that the system can then perform tokenization and further linguistic processing.),
converting the tokenized codes to scalar values,
performing frequency filtering to emphasize scalar values based on a frequency the scalar values appear in a set of data objects while de-emphasizing scalar values based on a frequency the scalar values appear in a group of sets of data objects(6.2.2: Describes converting tokenized strings to scalar values by assigning each token a numeric tf-idf weight, where the weight is computed as a real-valued scalar for each term in each document. The tf-idf weight is higher when a token appears many times within a document and lower when the token appears frequently across many documents, this emphasizes and de-emphasizes values based on term frequency patterns. The idf portion of tf-idf penalizes terms that occur in a large number of documents in the corpus, corresponding to de-emphasize values appearing frequently in a group of sets of data objects.),
saving the filtered scalar values as input variables, and
providing the input variables to the trained machine learning model to generate output variables(6.2.2: Further describes that after computing the tf-idf scalar weights for each token, a document is represented as a vector of real-valued components with one numeric value per term. This vector is then stored and used as the feature representation (input variables) for machine learning and classification algorithms.),
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Malone in view of Zhang, in view of Chen’s machine learning model architecture with Manning’s reference retrieval techniques. Doing so have enabled the pipeline to label data and transform strings, notes and other patient data with preprocessing techniques for enhance processing and machine learning capabilities.
Regarding claim 11, which recites substantially the same limitations as claim 1, to perform system instructions on computer hardware, respectively, and is therefore rejected on the same premise.
Regarding claim 19, which recites substantially the same limitations as claim 1, to perform system instructions on computer hardware, respectively, and is therefore rejected on the same premise.
Regarding claim 20, which recites substantially the same limitations as claim 1, to perform system instructions on computer hardware, respectively, and is therefore rejected on the same premise.
Claim(s) 2-4, and 21-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Malone et al. (US 20190325995 A1, referred to as Malone), in view of Zhang (US 11182691 B1, referred to as Zhang), in view of Manning et al. (“An Introduction to Information Retrieval” , referred to as Manning ), in view of Ding (20240250979 A1, referred to as Ding).
Regarding claim 2, Malone in view of Zhang in view of Manning teaches, the system of claim 1.
Although Malone in view of Zhang in view of Manning teaches claim 1, it does not teach, automatically determining optimal hyperparameters for the trained machine learning model and configuring the trained machine learning model with the optimal hyperparameters.
Ding teaches, automatically determining optimal hyperparameters for the trained machine learning model; and
configuring the trained machine learning model with the optimal hyperparameters ([0076-0082]: Describes automatically determining optimal hyperparameters by training multiple machine learning models, evaluating each model using performance metrics such as accuracy and false positive/false negative rates, and selecting the model configuration that performs best. The training system tunes the model(s) stating that models include “hyperparameters that are tunable” and that the system tunes and retrains the model to improve performance. Once evaluation identifies the best-performing model, the system selects and uses that model configuration, configuring the trained machine learning model with the optimal hyperparameters.).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Malone in view of Zhang, in view of Manning’s machine learning model architecture with Dings hyperparameter determination and model configuration. Doing so have enabled the system to optimize learning parameters with enhanced inputs and tune the models with those inputs.
Regarding claim 3, Ding further teaches, wherein determining the optimal hyperparameters includes:
loading baseline hyperparameters;
configuring the trained machine learning model with the baseline hyperparameters([0082]: Describes that for training a model the training system 601 can tune the model(s) with hyperparameters, indicating that the hyperparameters must be loaded in order to be used on the tuning.);
running the configured machine learning model to determine baseline metrics([0076-0081]: Describes how once a machine learning model is configured, the training system runs the model on a training and/or testing data set and evaluates its performance using defined metrics such as accuracy, false positive rate, false negative rate, and runtime. These indicate a baseline metric from running the model before any tuning changes.); and
in response to the baseline metrics being above a threshold, saving the baseline hyperparameters as the optimal hyperparameters([0076-0082]: Describes evaluating performance metrics and selecting the best configuration. The model is chosen whose metrics surpass others (above a threshold). The chosen configuration (optimal hyperparameters) is saved then for further use,).
Regarding claim 4, Ding further teaches, wherein determining the optimal hyperparameters includes, in response to the baseline metrics being at or below the threshold:
adjusting the baseline hyperparameters([0074-0082]: Describes that the training system tunes (adjusts) hyperparameters when the model’s performance is not satisfactory. The model selection workflow determines that if the performance is not acceptable, the system will not keep the current configuration),
reconfiguring the trained machine learning model with the adjusted hyperparameters([0074-0082]: Describes that the system tunes, the parameters into hyperparameters based on retraining them if they are not desired to create an improved configuration. When the performance of a model does not meet the desired criteria, the system alters the models hyperparameters and the retrains it to improve performance.),
running the reconfigured machine learning model to determine updated metrics([0074-0082]: Describes that after adjustment of the hyperparameters, the training process retrains the model and again revaluates performance while the model is running on training or testing data, using defined metrices such as accuracy, false positive rate, false negative rate, and runtime.) and
in response to the updated metrics being more optimal than the baseline metrics, saving the updated metrics as the optimal hyperparameters([0074-0082]: Describes) the training system evaluates model performance based on defined metrics and compares the results across different model configurations. The system selects “a machine learning model based on the comparison” when one configuration demonstrates better performance. The system tunes and retrains the model to improve performance.).
Regarding claim 21, which recites substantially the same limitations as claim 3, to perform system instructions on computer hardware, respectively, and is therefore rejected on the same premise.
Regarding claim 22, which recites substantially the same limitations as claim 3, to perform system instructions on computer hardware, respectively, and is therefore rejected on the same premise.
Regarding claim 23, which recites substantially the same limitations as claim 4, to perform system instructions on computer hardware, respectively, and is therefore rejected on the same premise.
Regarding claim 24, which recites substantially the same limitations as claim 4, to perform system instructions on computer hardware, respectively, and is therefore rejected on the same premise.
Regarding claim 25, which recites substantially the same limitations as claim 4, to perform system instructions on computer hardware, respectively, and is therefore rejected on the same premise.
Claim(s) 5, 6, and 12-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Malone et al. (US 20190325995 A1, referred to as Malone), in view of Zhang (US 11182691 B1, referred to as Zhang), in view of Chen et al. (WO2019022779 A1, referred to as Chen), in view of Manning et al. (“An Introduction to Information Retrieval” , referred to as Manning ), in view of Lin (US 20240012658 A1, referred to as Lin).
Regarding claim 5, Malone in view of Zhang in view of Manning teaches, the system of claim 1.
Although Malone in view of Zhang in view of Manning teaches claim 1, it does not teach, wherein the instructions include, in response to determining the output variables are above a threshold loading a second trained machine learning model, and providing the input variables to the second trained machine learning model to generate second output variables.
Lin teaches wherein the instructions include, in response to determining the output variables are above a threshold:
loading a second trained machine learning model([00-160019]: Describes training a first trained mode then a second and third. Loaded form the same input variables and the system dynamically chooses which one to use based on the largest output.), and
providing the input variables to the second trained machine learning model to generate second output variables([0116-0117]: Describes secondary output variables. ;[0122]: Describes that the system loads multiple trained learning models and provides the same set of input variables to each of them to generate respective output variables.).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Malone in view of Zhang, in view of Manning’s machine learning model architecture with Lin’s addition training models. Doing so have enabled the system to optimize learning by training further models to ensure a more robust output and narrow results.
Regarding claim 6, which recites substantially the same limitations as claim 5, and adding that loading a third trained machine learning model, and providing the input variables to the third trained machine learning model to generate third output variables (Lin describes loading three training models to generate further output [0016]), respectively, and is therefore rejected on the same premise.
Regarding claim 12, Malone teaches, the input variables include an identifier of an entity in a population ([0038-0041]: Describes that the system uses episode identifiers, patient identifiers and episode snapshots.);
the output variables include a score for the entity indicated by the identifier ([0089-0090]: Describes how predictions are generated per episode/patient, which are scores associated with the same episode identifier.); and
Lin teaches, the score indicates a likelihood of a feature of merit exceeding a threshold( [0012]:Describes an output variable corresponding to a score which is evaluated against a threshold and the system behaves differently when the score crosses a particular threshold.).
Regarding claim 13, Lin teaches, wherein the instructions include:
generating a plurality of scores for a plurality of entities in the population ([0116-0117]:Describes generating scores for entities by outputting secondary output variables from model scoring modules and further synthesizing them into tertiary and percentile scores saved in a score database. The tertiary outcome variables are for a member of a population and computing percentile scores for a given member relative to that population. A plurality of scores is generated for the plurality of entities in the population.); and
Manning teaches, clustering the plurality of scores into a plurality of clusters (16.4 Describes clustering numerical values using standard clustering algorithms, which partition the input values into K clusters.).
Regarding claim 14, Manning teaches, wherein the plurality of clusters is three clusters (16.4: Describes a K means algorithm shows that it can be used for any number of clusters as needed).
Regarding claim 15, Manning teaches, wherein:
the plurality of clusters includes a particular cluster associated with a greatest risk(provides the clustering and Lin [0018] describes score thresholds to signal severity/higher condition probability, meaning a higher score means its more sever and more risk.); and
Lin teaches, the instructions include adapting a graphical user interface in response to the score being assigned to the particular cluster ([0006]:Describes that the graphical user interface adapts based on the score which is assigned to a particular cluster.).
Regarding claim 16, Lin teaches, wherein the score is a value between zero and one hundred inclusive ([0117]: Describes generating a percentile score for each entity, derived by comparing the entities tertiary outcome variable against a population of members. A percentile score is a normalized value between 0-100, inclusive.).
Regarding claim 17, Malone teaches, wherein:
the population includes entities that consume services ( [0021-0025]: Describes that each episode (patient) consumes medical/hospital services.; [0038-0043]: Describes that episode snapshots include vitals, procedures, labs and other information form a medical visit for a patient.); and
the feature of merit is a measure of service consumption of the entity ([0021-0022]: Describes a length of stay (LOS) which is an outcome predicted per episode, It is a remaining LOS of a quantitative measure of hospital services consumed.).
Regarding claim 18, Malone teaches, wherein:
the population includes entities that coordinate services ([0016-0019]: Describes a population that includes entities who coordinate services, such as clinicians and care providers who use the system’s predictions to guide clinical decisions and service planning.); and
the feature of merit is an amount of services advised by the entity ([0021-0022]: Describes predicating outcomes such as remaining length of stay (LOS), which is a quantitative measure used by clinicians to determine the amount of hospital services a patient should receive.).
Claim(s) 7-10, 26, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Malone et al. (US 20190325995 A1, referred to as Malone), in view of Zhang (US 11182691 B1, referred to as Zhang), in view of Chen et al. (WO2019022779 A1, referred to as Chen), in view of Manning et al. (“An Introduction to Information Retrieval” , referred to as Manning ), in view of Lu )US 20220172812 A1 referred to as Lu).
Regarding claim 7, Malone in view of Zhang in view of Manning teaches, the system of claim 1.
Although Malone in view of Zhang in view of Manning teaches claim 1, it does not teach, wherein the trained machine learning model includes a light gradient-boosting machine model.
Lu teaches, wherein the trained machine learning model includes a light gradient-boosting machine model ([0137]: Describes using a Light Gradient Boosting Machine in its models.).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Malone in view of Zhang, in view of Manning’s machine learning model architecture with Lu’s Light Gradient Boosting Machine. Doing so have enabled the system to enhance the output of additional models to further refine training models.
Regarding claim 8. Lu teaches, wherein the trained machine learning model includes a mixed effects random forests model with a light gradient-boosting machine regressor([0137]: Describes using a nonlinear mixed effects model with the LGBM regressor.).
Although Lu teaches a mixed effects LGBM regressor it does not use random forest. Ding [0074] does use random forest with other models including “logistic regression, decision tree, random forest, Extreme Gradient (XG) boosting, ridge regression, support vector machine (SVM), natural language processing (NLP)-based model, or any other appropriate type of machine learning model”.
Regarding claim 9, Zhang teaches, The system of claim 8 wherein input variables include a third bin and a fourth bin (Col. 54, lines 37-58: Describes multiple categories being used in the system these categories correspond to bins. Although these only correspond to 3 bins, Col 55, lines 22-55 does describe 4 different categories as categories of observation records, may be referred to herein as category-based sampling, category-dependent sampling, class-based sampling, or class-dependent sampling”, the disclosure describes that multiple categories may be used corresponding to more than 3 categories for use in the machine learning model.).
Regarding claim 10. The system of claim 9 wherein the input variables of the third bin are assigned to fixed effects and the input variables of the fourth bin are assigned to mixed effects (Zhang teaches dividing input into multiple categories, where Ding teaches using multiple machine learning models, to include random forests and gradient boosting. A person of ordinary skill in the art would recognize that mixed-effects modeling frameworks allocate different feature sets to fixed-effects and random-effects components, with Random Forest models commonly used to estimate fixed effects and gradient boosting models used to estimate rand effects. Lu teaches the Light Gradient Boosting Model, an implementation suitable for the random-effects regressor. Assigning fixed and random effects to specific bins/categories would be an obvious design for a model).
Regarding claim 26, which recites substantially the same limitations as claims 7 and 8, to perform system instructions on computer hardware, respectively, and is therefore rejected on the same premise.
Regarding claim 27, Malone teaches, wherein the output variables include at least one of (i) cost estimations for treatment groups and (ii) cost estimations for treatments ([0016-0022]: Describes output variables that predict remaining length of stay, discharge destination, number of rehab sessions, and care intensity. LOS and number of rehab sessions are direct measures of service utilization, which are used by hospitals and external facilities to perform resource allocation and planning. These resource utilization predictions correspond to cost estimators for treatment groups and for treatments.).
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 1-4, 7-8, and 11-21 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-24 of copending Application No. US 20240256944 A1 in view of Chen et al. (WO201902779 A1, referred to as Chen), in view of Moen et al. ("Care Episode Retrieval", referred to as Moen).
This is a provisional nonstatutory double patenting rejection.
Instant Application
Patent No. US 20240256945 A1
The Instant application fails to particularly teach generating input variables by unionizing condition-specific data objects using common identifiers to generate merged condition-specific data, tokenizing each alphanumeric code, converting the tokenized codes to scalar values, and performing frequency filtering.
However, Chen teaches converting raw electronic health records from different sources and formats into standardized FHIR resources, assembling patient specific sets/bundles of resources, and using medical diagnosis/procedure codes, including ICD-9/10 codes, CPT codes, and CCS code mappings (page 15 lines 29-26, page 16 lines 1-7, and page 17 lines 7-25 ). Moen teaches tokenization(pg. 120-121, Experiments), frequency filtering(pg. 120, Computing care episode similarity) and episode archiving(pg. 116, Introduction).
It would have been obvious to a person of ordinary skill in the arts at the times of the applicant’s invention to modify the teachings of the instant application by incorporating generating input variables by unionizing condition-specific data objects using common identifiers to generate merged condition-specific data, tokenizing each alphanumeric code, converting the tokenized codes to scalar values, and performing frequency filtering as taught by Chen, in view of Moen for the purpose of data preparation to train and optimize the model performance and ensure consistent feature encoding.
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Claims 1-27 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-27 of copending Application No. US 20240256987 A1 in view of Moen et al. ("Care Episode Retrieval", referred to as Moen). Both describe a system and method for loading over and under sampling, merging data, configuring model with baseline hyperparameters determine performance metrics relative to a threshold and adjusting or saving hyperparameters accordingly.
This is a provisional nonstatutory double patenting rejection.
Instant Application
Patent No. US 20240256987 A1
The Instant application fails to particularly teach input variables comprising non-standard identifiers of conditions, output variables comprising standardized identifiers of conditions, output variables including, predicated standard treatment regimes, confidence levels, likelihood of switching a treatment regime, likelihood of immunotherapy, chemotherapy or hormonal therapy, and probabilities of continuing, restarting or discontinuing a treatment regime.
However, Moen teaches input variables comprising non-standard identifiers of conditions(Page 2, Introduction: Describes that clinical notes contain “highly domain-specific terminology… variations in the language and terminology used in sub-domains within and across health care organizations…” this corresponds to non-standard identifiers referring to clinicians using inconsistent, non-normalized phrases for diagnosis), output variables comprising standardized identifiers of conditions (Page 3, Data Describes ICD-10 assignments, mapping non-standard free-text notes to a standardized disease classification), output variables including (Page 2, Introduction: Describes the model is intended to assist clinicians by finding similar prior patients to examine “what similar patients have received in terms of medication and further treatment… what clinical practice guidelines have been utilized…” this correspond to retrieving standardized treatment regimens with similarity scores functioning as confidence levels), predicated standard treatment regimes, confidence levels, likelihood of switching a treatment regime, likelihood of immunotherapy, chemotherapy or hormonal therapy, and probabilities of continuing, restarting or discontinuing a treatment regime (Page 2, Introduction: Describes probabilistic similarity scores between episodes, semantic clustering of episodes by diagnosis and treatment and retrieval of past episodes with known outcomes “what similar patients have received in terms of medication and further treatment, what related issues such as bi-conditions or risks occurred…” )these similarity scores are likelihoods and probabilities.)
It would have been obvious to a person of ordinary skill in the arts at the times of the applicant’s invention to modify the teachings of the instant application by incorporating tokenization, frequency filtering and episode archiving as taught by Moen’s for the purpose of data preparation to train and optimize the model performance and ensure consistent feature encoding.
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Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DONALD T RODEN whose telephone number is (571)272-6441. The examiner can normally be reached Mon-Thur 8:00-5:00 EST.
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/D.T.R./Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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