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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed 12-10-2025 has been entered.
Status of Claims
This action is a non-final rejection
Claims 1-4, 6-7, 9-14, 16-17, 19-20 are pending
Claims 5, 8, 15, 18 were cancelled
Claims 1, 11 were amended
Claims 1-4, 6-7, 9-14, 16-17, 19-20 are rejected under 35 USC § 101
Priority
Acknowledgement is made of Applicant’s claim for a domestic priority date of 7-12-2022
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12-8-2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-7, 9-14, 16-17, 19-20 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1-4, 6-7, 9-14, 16-17, 19-20 the claims recite an abstract idea of “predicting a healthcare outcome for a patient based on data comprising a plurality of healthcare events”.
Independent Claims 1 & 11 are rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claims 1 & 11 recites a computer implemented method and a system of method of predicting a healthcare outcome for a patient based on data comprising a plurality of healthcare events.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES):
obtaining a dataset comprising health data, the health data comprising a plurality of healthcare events in a nested data structure;
partitioning the nested data structure into a first portion comprising temporal healthcare events and a second portion comprising static patient data transforming, by flattening the first portion of the nested data structure from the nested data structure into a first flattened, data structure comprising:
transforming, by flattening the second portion of the nested data structure from the nested data structure into a second flattened, .. data structure comprising:
training a model using the events table and the traits table, the ... model comprising:
receive the events table and the traits table and distill the events table and the traits table into a lower-dimensional representation;
predict a health outcome for a patient based on a first output and forecast one or more observation values from the events table based on a second output
predicting, using the model and one or more additional healthcare events associated with a patient, a health outcome for the patient.
generating, based on the predicted health outcome, an ... notification configured to be transmitted to a user ... to facilitate prioritization of clinical attention for the patient
belong to the grouping of mental processes under concepts performed in the human mind as it recites “predicting a healthcare outcome for a patient based on data comprising a plurality of healthcare events”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “predicting a healthcare outcome for a patient based on data comprising a plurality of healthcare events”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claims 1 & 11 recite:
a Fast Healthcare Interoperability Resources (FHIR) standard;
machine learning-compatible data structure;
training a machine learning model;
a shared encoder network configured to receive the events table and the traits table;
a primary classifier network configured to predict a health outcome for a patient;
an auxiliary decoder network configured to forecast one or more observation values;
wherein the shared encoder network, primary classifier network, and auxiliary decoder network are co-trained;
using the trained machine learning model;
an electronic notification;
user device.
Claim 1 recites:
A computer-implemented method executed by data processing hardware that causes the data processing hardware to perform operations;
Claim 11 recites:
data processing hardware;
memory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations;
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0040-0050]. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two,
Claims 1 & 11 recite:
a Fast Healthcare Interoperability Resources (FHIR) standard;
machine learning-compatible data structure;
training a machine learning model;
a shared encoder network configured to receive the events table and the traits table;
a primary classifier network configured to predict a health outcome for a patient;
an auxiliary decoder network configured to forecast one or more observation values;
wherein the shared encoder network, primary classifier network, and auxiliary decoder network are co-trained;
using the trained machine learning model;
an electronic notification;
user device.
Claim 1 recites:
A computer-implemented method executed by data processing hardware that causes the data processing hardware to perform operations;
Claim 11 recites:
data processing hardware;
memory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations.
Amount to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0040-0050]. (refer to MPEP 2106.05(f)) Accordingly, even in combination the additional elements of the claim do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Dependent Claims:
Step 2A Prong One: The following dependent claims recite additional limitations that further define the abstract idea of “predicting a healthcare outcome for a patient based on data comprising a plurality of healthcare events”. These claim limitations include:
Claim 2 & 12: wherein obtaining the dataset comprises:
receiving a training request defining a data source of the dataset; and
retrieving the dataset from the data source;
Claim 3 &13: wherein the operations further comprise normalizing one or more codes of the health data;
Claim 4 & 14: wherein the operations further comprise normalizing one or more units of the health data;
Claim 6 & 16: wherein the traits table comprises patient demographics;
Claim 7 & 17: wherein the events table represents the dataset as a structured time-series;
Claims 9 & 19; wherein the operations further comprise generating a user-configurable trait table comprising context-specific static features indexed by the unique identifier per patient encounter;
Claims 10 & 20: wherein generating the user-configurable trait table comprises receiving the context-specific static features from a user;
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). There are no additional elements recited by the dependent claims.
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, There are no additional elements recited by the dependent claims.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
Heimendinger (US 20100131457 A1) - FLATTENING MULTI-DIMENSIONAL DATA SETS INTO DE-NORMALIZED FORM– teaches: Performance metrics data in a multi-dimensional structure such as a nested scorecard matrix is transformed into a flat structure or de-normalized for efficient querying of individual records. Each dimension and header is converted to a column and data values resolved at intersection of dimension levels through an iterative process covering all dimensions and headers of the data structure. A key corresponding to a tuple representation of each cell or a transform of the tuple may be used to identify rows corresponding to the resolved data in cells for further enhanced query capabilities.
Conort; (US 20220076164 A1) - AUTOMATED FEATURE ENGINEERING FOR MACHINE LEARNING MODELS– teaches: Training computer models by generating time-aware training datasets is provided. A system receives a secondary dataset to be combined with a primary dataset for generation of a training dataset. The primary dataset includes a plurality of data records where at least one data record corresponds to a time-of-prediction value corresponding to a timestamp at which at least one data record was used to generate a prediction. The secondary dataset includes a plurality of features where at least one feature corresponds to a timestamp value. The system selects a feature within the secondary dataset with a timestamp that precedes or matches a time-of-prediction value for a corresponding data record within the primary dataset. The system generates the training dataset that includes the primary dataset and the selected feature. The system trains a model using the generated training dataset.
Tran (WO 2014201515 A1) - MEDICAL DATA PROCESSING FOR RISK PREDICTION– teaches: A computer system for processing medical data may include an input module, an extractor, a selector, a trainer, and a probability generator. The input module may be configured to: import raw medical data from one or more computer-readable files, wherein said raw medical data represent a plurality of electronic medical records (EMRs) for a plurality of persons, said EMRs including descriptions of medical occurrences, times associated with the medical occurrences, and medical outcomes for the persons, and generate events data representing a timeline of events for each person, wherein each event includes an event type, an event time, and an event value, wherein said event types, said event times and said event values are determined using pre-selected event generating rules applied to the descriptions and times of the medical occurrences. The extractor may be configured to receive the events data, and to extract feature values from the timelines by applying a filterbank with filters of different temporal widths to the timelines, wherein the filters extract said feature values using the event values of those of the events with event times within the temporal widths of the filters. The selector may be configured to: receive the extracted feature values from the extractor, each feature value being associated with a feature defined by one of the filters applied to one of the event types, and select ones of the features that are indicative of a medical outcome in a training data set of the raw medical data. The trainer may be configured to: receive the selected features, and training data representing the medical occurrences and the medical outcomes, and train a classifier using the selected features and the training data, wherein the classifier is configured to classify a person into a one of a selected plurality of probability classes associated with the medical outcome based on that person's medical data representing the medical occurrences and associated times. The computer system may include a probability generator configured to extract values corresponding to the subset of selected features from a person's raw medical data, and to generate a probability value of the outcome for the person using the extracted values in the numerical model of probability. The computer system may include a visualisation module configured to generate filtered record data representing medical occurrences from a selected person's medical data using a subset of selected features that are indicative of a medical outcome.
Chen (US 20200388358 A1) - Machine Learning Method For Generating Labels For Fuzzy Outcomes– teaches: A machine learning method is described for generating labels for members of a training set where the labels are not directly available in the training set data. In a first stage of the method an iterative process is used to gradually build up a list of features (“partition features” herein) which are conceptually related to the class label using a human-in-the loop (expert). In a second part of the process we generate labels for the members of the training set, build up a boosting model using the labeling to come up with additional partition features, score the labeling of the training set members from the boosting model, and then with the human-in-the-loop evaluate a labels assigned to a small subset of the members depending on their score. The labels assigned to some or all of those members in the subset may be flipped depending on the evaluation. The final outcome of the process is an interpretable model that explains how the labels were generated and a labeled set of training data.
Mossin (US 20190034591 A1) – System And Method For Predicting And Summarizing Medical Events From Electronic Health Records - teaches: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order. An electronic device configured with a healthcare provider-facing interface displays the predicted one or more future clinical events and the pertinent past medical events of the patient.
Response to Arguments
The Applicant's arguments filed 12-10-2025, have been fully considered but not found
persuasive.
Applicant amended independent claims 1, 11 as posted in the above analysis with additions underlined and deletions as .
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 101.
Several steps are taken in the analysis as to whether an invention is rejected under 101. The first step is to determine if the claim falls within a statutory category. In this case it does for claims 1 and 11 since the claims recite a method, and apparatus to predict the occurrence of future disease in animals.
The second step under 2A prong one is to determine if the claims recite an abstract idea, which would be the case if the invention can be grouped as either: a) mathematical concepts; (b) mental processes; or (c) certain methods of organizing human activity (encompassing (i) fundamental economic principles, (ii) commercial or legal interactions or (iii) managing personal behavior or relationships or interactions between people). The current invention is classified as an abstract idea since it may be grouped as a mental process as it recites “predicting a healthcare outcome for a patient based on data comprising a plurality of healthcare events”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “predicting a healthcare outcome for a patient based on data comprising a plurality of healthcare events”.
The third step under 2A Prong Two is to determine if additional elements in the claim imposes a meaningful limit on the abstract idea in order to integrate it into a practical idea. The current invention does not represent a practical idea since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
the fourth step under 2B is to determine if additional elements of the claim provide an inventive concept. An invention may be classified as an inventive concept if a computer-implemented processes is determined to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic, and non-conventional even if generic computer operations on a generic computing device is used to implement the abstract idea.
Step 2A Prong ONE
The Applicant argues that independent claims 1, 11 do not recite an abstract idea as posted in the Office Action since they do not recite a mental process since when analyzed correctly, the character of claim 1 (and similarly claim 11) as a whole is directed to a specific, technological method for transforming complex healthcare data structures and utilizing a particularized machine learning architecture to predict patient health outcomes and generate actionable electronic notifications. This process is intrinsically tied to the specific operations of computer systems and data processing hardware and involves concrete data transformations and computational structures that cannot practically be performed in the human mind.
The Examiner disagrees since the Applicant’s arguments are not persuasive. The method to select the abstract idea is to strip the additional elements from the claims. As seen below the recited boldened words constitute the abstract idea after stripping the un-boldened additional elements of amended limitation of claims 1 & 11:
obtaining a dataset comprising health data in a Fast Healthcare Interoperability Resources (FHIR) standard, the health data comprising a plurality of healthcare events in a nested data structure;
partitioning the nested data structure into a first portion comprising temporal healthcare events and a second portion comprising static patient data transforming, by flattening the first portion of the nested data structure from the nested data structure into a first flattened, machine learning-compatible data structure comprising:
an events table comprising the plurality of healthcare events, the events table indexed by time and a unique identifier per patient encounter;
transforming, by flattening the second portion of the nested data structure from the nested data structure into a second flattened, machine learning-compatible data structure comprising;
a traits table comprising static data, the traits table indexed by the unique identifier per patient encounter;
training a machine learning model using the events table and the traits table, the machine learning model comprising:
a shared encoder network configured to receive the events table and the traits table and distill the events table and the traits table into a lower-dimensional representation;
a primary classifier network configured to predict a health outcome for a patient based on a first output of the shared encoder network; and
an auxiliary decoder network configured to forecast one or more observation values from the events table based on a second output of the shared encoder network, wherein the shared encoder network, primary classifier network, and auxiliary decoder network are co-trained;
predicting, using the trained machine learning model and one or more additional healthcare events associated with a patient, a health outcome for the patient;
generating, based on the predicted health outcome, an electronic notification configured to be transmitted to a user device to facilitate prioritization of clinical attention for the patient.
The selected abstract idea (boldened limitations) of claims 1 & 11 can be implemented by pencil and paper and thus belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “predicting a healthcare outcome for a patient based on data comprising a plurality of healthcare events”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “predicting a healthcare outcome for a patient based on data comprising a plurality of healthcare events”. (refer to MPP 2106.04(a)(2)). Accordingly independent claims 1 and 11 recite an abstract idea.
Step 2A Prong TWO
The Applicant argues that even if the invention recites an abstract idea it is directed to patentable subject matter since it integrates the abstract idea into a practical application. The Applicant further argues that the claims are directed toward a specific, technical method for processing complex healthcare data and generating actionable clinical insights, providing a concrete technical solution to technical problems inherent in utilizing standardized but structurally complex health records for predictive modeling and clinical decision support. The Applicant lists the technical problems addressed by the invention as:
Utilizing health data in the Fast Healthcare Interoperability Resources (FHIR) standard, which, as noted in ¶ [0025] of the Specification, "is typically in a highly nested format that allows repeated entries at different levels," rendering it generally unusable for many machine learning models that "typically require 'flat' (i.e., data that is not nested) data as input.".
The claims overcome these technical problems by reciting specific, technical steps that are inextricably tied to the manipulation of computer data structures and the functional improvement of computer-aided prediction systems. The limitations of claim 1 (and similarly claim 11) represent a specific, technological implementation that transforms any alleged initial data gathering and analysis into a concrete, automated data processing and clinical support method.
As another example, the amended claim recites a specific, unconventional machine learning model architecture:"a shared encoder network configured to receive the events table and the traits table; a primary classifier network configured to predict a health outcome for a patient based on a first output of the shared encoder network; and an auxiliary decoder network configured to forecast one or more observation values from the events table based on a second output of the shared encoder network, wherein the shared encoder network, primary classifier network, and auxiliary decoder network are co-trained."
As yet another example, the step of "generating, based on the predicted health outcome, an electronic notification configured to be transmitted to a user device to facilitate prioritization of clinical attention for the patient" ties the computational prediction to a concrete, practical application in clinical workflow. This is not merely displaying data; it is the generation of a specific output (an electronic notification) for a specific purpose
Employs a particular co-trained, multi-network machine learning architecture tailored for complex healthcare data; and 3) generates an actionable electronic notification to directly influence clinical prioritization. This sequence is tied to the specific technological environment of processing standardized healthcare data and applying advanced machine learning techniques for improved clinical support. It is far removed from any generic instruction to use a computer or a process performable entirely in the human mind.
The Examiner disagrees with the Applicant since the Applicant’s arguments are not persuasive.
The Examiner restates that claims 1 & 11 do not integrate the abstract idea into a practical application.
Neither claim 1 or 11 recite additional elements that impose a meaningful limit on the abstract idea:
Claims 1 & 11 recite:
a Fast Healthcare Interoperability Resources (FHIR) standard;
machine learning-compatible data structure;
training a machine learning model;
a shared encoder network configured to receive the events table and the traits table;
a primary classifier network configured to predict a health outcome for a patient;
an auxiliary decoder network configured to forecast one or more observation values;
wherein the shared encoder network, primary classifier network, and auxiliary decoder network are co-trained;
using the trained machine learning model;
an electronic notification;
user device.
Claim 1 recites:
A computer-implemented method executed by data processing hardware that causes the data processing hardware to perform operations;
Claim 11 recites:
data processing hardware;
memory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations.
The additional elements as recited above amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Support for this can be found in the specification, paragraphs (0040-0049). Accordingly, the claim as a whole do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Providing a concrete technical solution to technical problems inherent in utilizing standardized but structurally complex health records for predictive modeling and clinical decision support is not enough to classify the claims as integrated into a practical application..
In order to integrate the abstract idea into a practical application the additional elements should be shown to impose a meaningful limit on the abstract idea. A colloquial interpretation of a practical application is not enough.
In order to integrate the abstract idea into a practical idea the Applicant could demonstrate at least one of the conditions enumerated below applies:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
The Applicant has not demonstrated any of the above listed conditions.
Regarding Step 2B
Similar to the analysis under Step 2A Prong Two, the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Support for this can be found in the specification, paragraphs (0040-0049). Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
In order evaluate whether the claim recites additional elements that amount to an inventive concept what could be shown is:
Adding a specific limitation (unconventional other than what is well-understood, routine, conventional (WURC) activity in the field - see MPEP 2106.05(d)
The Applicant has not demonstrated the above listed condition.
As a result, the Examiner restates the rejection of the invention under 35 USC §101.
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 103.
The Applicant argues that the following amended limitations of claims 1 and 11 are not taught by the cited prior art:
partitioning the nested data structure into a first portion comprising temporal healthcare events and a second portion comprising static patient data
transforming, by flattening from the nested data structure into a first flattened, machine learning-compatible data structure comprising an events table
transforming, by flattening the second portion of the nested data structure from the nested data structure into a second flattened, machine learning-compatible data structure comprising a traits table
machine learning model comprising a shared encoder network configured to receive the events table and the traits table and distill the events table and the traits table into a lower-dimensional representation
a primary classifier network configured to predict a health outcome for a patient based on a first output of the shared encoder network
an auxiliary decoder network configured to forecast one or more observation values from the events table based on a second output of the shared encoder network
Regarding the first limitation Heimendinger provides no disclosure or suggestion of partitioning the nested data structure into a first portion comprising temporal healthcare events and a second portion comprising static patient data, as claimed. Instead, Heimendinger describes a monolithic process where 'each dimension and header' of the input 'scorecard matrix' is converted into a column in a single 'two- dimensional data structure.' Id. at [0041] and [0046] and FIG. 5. Heimendinger's process is not concerned with the underlying nature of the data (e.g., temporal vs. static) but rather with a comprehensive conversion of all structural elements of its input matrix into a single, query-able table.
Regarding the second and third limitations, Heimendinger describes generating a single, unified output table, Heimendinger also fails to disclose or suggest transforming, by flattening a first portion of the nested data structure from the nested data structure into a first flattened, machine learning- compatible data structure comprising an events table and separately transforming, by flattening a second portion of the nested data structure from the nested data structure into a second flattened, machine learning-compatible data structure comprising a traits table. Specifically, Heimendinger teaches a one-to-one structural transformation, not a one-to-many transformation based on a preliminary partitioning of the source data.
Regarding the Fourth, Fifth and Sixth limitations Conort provides no disclosure or suggestion of a machine learning model comprising a shared encoder network configured to receive the events table and the traits table and distill the events table and the traits table into a lower-dimensional representation, a primary classifier network configured to predict a health outcome for a patient based on a first output of the shared encoder network, and an auxiliary decoder network configured to forecast one or more observation values from the events table based on a second output of the shared encoder network, as claimed. Conort's disclosure is limited to the pre-processing of data to generate a 'training dataset' before a model is ever trained. Conort is entirely silent on the internal, computational architecture of the machine learning model that will subsequently ingest that data. Furthermore, because Conort does not disclose the claimed three-network architecture, Conort fails to disclose or suggest that the shared encoder network, primary classifier network, and auxiliary decoder network are co-trained, as claimed. Conort's teachings are directed to the composition of a 'training dataset,' not the specific, multi-task computational structure of the model itself.
Tran and Chen are likewise deficient and do not remedy the failures of Heimendinger and Conort. Tran discloses a system for processing 'raw medical data' to predict a medical outcome. Tran describes using an 'input module' to process data from various sources, including generating 'timelines' from temporal data and importing 'static' information. Tran fails to disclose or suggest starting with a nested data structure and therefore does not teach or suggest the claimed partitioning of such a structure and the separate transforming, by flattening operations on the resulting portions. Furthermore, Tran fails to disclose the claimed three-network architecture comprising a shared encoder network, a primary classifier network, and an auxiliary decoder network that are co-trained.
Chen, while mentioning the FHIR format as one possible standard for data conversion, is focused on a human-in-the-loop process for generating class labels to build interpretable models. Chen similarly fails to disclose or suggest the claimed partitioning of a nested structure, the separate flattening transformations into distinct events and traits tables, or the specific co-trained, encoder-classifier-decoder model architecture.
Therefore, the references fail to disclose or suggest all of the claim limitations of claims 1 and 11.
The Examiner agrees with the Applicant, Furthermore the Examiner has not found additional art that would in combination with the previously cited references successfully teach the added amended limitations. As a result, the Examiner withdraws the 35 USC §103 rejection. Prior art reference of record that is most closely related to the claim limitation recited above is listed under “Prior Art Made of Record”
For reasons of record and as set forth above, the examiner maintains the rejection of claims 1-4, 6-7, 9-14, 16-17, 19-20 as being directed to a judicial exception without significantly more, and thereby being directed to non-statutory subject matter under 35 USC §101. In reaching this decision, the Examiner considered all evidence presented and all arguments actually made by Applicant.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE L MACCAGNO whose telephone number is (571)270-5408. The examiner can normally be reached M-F 8:00 to 5:00.
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687