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 .
DETAILED ACTION
Response to Amendment
The present Office Action is in response to the Request for Continued Examination dated 11 June 2026.
In the amendment dated 11 June 2026, the following occurred: Claims 1, 14, 16, 29, and 30 have been amended; Claims 12, 13, 27, and 28 have been cancelled; Claim 49 is new.
Claims 1-11, 14-26, 29, 30, 32-41, 44, and 46-49 are pending.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 on 11 June 2026 has been entered.
Priority
This application claims priority to U.S. Provisional Patent Application No. 63/425,728 dated 16 November 2022.
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 1-11, 14-26, 29, 30, 32-41, 44, and 46-49 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 16, and 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a system and method for determining patient access to a healthcare facility, which are within a statutory category (see 112(f) interpretation regarding Claim 30).
Step 2A1
The limitations of (Claim 16 being representative) extracting health related data from a data source […] to form extracted health data, wherein the health data includes patient encounter data, lost appointment data, and schedule data, generating a plurality of data pipelines […] for conveying the extracted health data including the patient encounter data, lost appointment data, and schedule data, generating a plurality of data pipelines for conveying the extracted health data, wherein generating the plurality of data pipelines includes: generating a patient encounter standardization pipeline […] to convey patient encounter data and to standardize the patient encounter data using a standardization technique; generating a temporal master file creation pipeline […] to create one or more temporal master files from the patient encounter data and from provider schedule data, wherein the temporal master files store historical versions of master data including healthcare practice specialties, payors, appointment types, appointment statuses, and appointment cancellation reasons; generating a final master file creation pipeline based on the one or more temporal masters file […] to create a final master file by applying one or more mapping techniques that create category fields and assign new identifiers to each value across the master files; generating a file normalization pipeline […] to normalize and standardize files associated with the patient encounter data and provider schedule data; generating a patient encounter final output pipeline […] to create and convey a final patient encounter output including fields for identifying the patient's last encounter; and generating a provider schedule final output pipeline […] to create and convey a final provider schedule output including key data elements for calculating patient appointment session length and appointment capacity, storing at least a portion of the extracted health data conveyed over one or more of the plurality of data pipelines in a healthcare-specific common data model to form stored health data, wherein the healthcare-specific common data model includes a plurality of tables for organizing and storing the extracted health data wherein the plurality of tables includes a patient encounter table configured as a first central table of the healthcare-specific common data model for storing patient encounter data, a provider schedule table configured as a second central table of the healthcare-specific common data model for storing provider schedule data, and a plurality of auxiliary tables configured to provide data to the patient encounter table and the provider schedule table, determining from at least the patient encounter data forming part of the health data stored a number of lost appointments that can be recovered by the healthcare facility, identify from the lost appointments an unavailable appointment time and a total recoverable appointment time that is recoverable from the unavailable appointment time, determine a number of potential available appointments by applying a preselected appointment time length to the total recoverable appointment time, apply a recovery factor to the number of potential available appointments to determine a number of actual available appointment time, and apply a reimbursement rate to the number of actual available appointment times to determine revenue generated by the actual available appointment times, applying one or more machine learning models to the stored health data to generate predictions therefrom, wherein the one or more machine learning models include at least one of a supervised learning model, an unsupervised learning model, a reinforcement learning model, a deep learning model, or a natural language processing model, and wherein the one or more machine learning models are trained using a multi-stage training process comprising: (a) training the machine learning model with a full dataset of the patient encounter data, lost appointment data, and schedule data, wherein training adjusts weights of the machine learning model based on patterns in the full dataset to form a trained machine learning model; and (b) tuning the trained machine learning model by retraining the trained machine learning model on a narrower dataset of the health data and adjusting one or more tuning parameters to perform a selected healthcare facility optimization task, and generating […output…] for displaying selected portions of the stored health data and the predictions, as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a processor/CRM, computer, or computer having specific programming (see previous 112(f) interpretation), each of which include a user interface (i.e., a display GUI) the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the various generic computer components, the claims encompass a person collecting and storing health data, determining a number of lost appointments that can be recovered, and generating predictions in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The Examiner notes that the “one or more machine learning models” has been defined by the Applicant as “at least one of a supervised learning model, an unsupervised learning model, a reinforcement learning model, a deep learning model, or a natural language processing model.” The Examiner notes that both logistic regression and decision trees are supervised machine learning models. As such, the “one or more machine learning models” encompasses simplistic mathematical models that are part of the rules or instructions that a person or persons would follow; a person having skill in the would readily interpret the noted machine learning models and associated training to represent part of the rules or steps for a human to perform. While these particular limitations may be considered mathematical relationships and/or mental process consistent with the analysis in Example 42, Claim 2, the claim as a whole is directed towards a method of organizing human activity.
The training and retraining (by adjusting tuning parameters) of a machine learning model is also recited in the claim. The type of training/retraining utilized by the claimed invention is not described by the Applicant. As such the Examiner is required to analyze the training and retraining step given the broadest reasonable interpretation. The step(s) performed to train and retrain the model/algorithm is/are considered to be part of the abstract idea because it/they fall(s) under data manipulations that humans perform (i.e., fitting a model to data and/or tuning parameters) and thus are interpreted to be part of the abstraction--the rules or instructions that fall under Certain Methods of Organizing Human Activity. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 12 (Fed. Cir. April 18, 2025) (finding that “[i]terative training using selected training material…are incident to the very nature of machine learning.”).
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a processor/CRM, computer, or computer having specific programming (see previous 112(f) interpretation), each of which include a user interface (i.e., a display GUI), that implements the identified abstract idea. These additional elements are not described by the applicant and are recited at a high-level of generality (i.e., a generic computer or components thereof) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites the additional element of using an extract, transform and load (“ETL”) technique. The use of ETL merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Assuming arguendo that data pipelines are additional elements and not part of the abstract idea, the use of data pipelines to ETL data also merely generally links the abstract idea to a particular technological environment or field of use. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a processor/CRM, computer, or computer having specific programming (see previous 112(f) interpretation), each of which include a user interface (i.e., a display GUI) to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer or components thereof. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using an extract, transform and load technique was determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Further, and for completeness, the Examiner also notes that utilizing ETL is well-understood, routine, and conventional in the art (see US 20130297536 A1 to Almosni et al. at Para. 0076; US 20190146970 A1 to Chamieh et al. at Para. 0140; US 20160179630 A1 to Halberstadt et al. at Para. 0013). Assuming arguendo that data pipelines are additional elements and not part of the abstract idea, the prior art of record above indicates that ETL is well-understood, routine, and conventional. The document “What is ETL?” thereafter indicates that ETL, by definition, includes the generation of pipelines in which data is standardized, files associating data are generated, data is stored in the files, and data is outputted. This is how ETL works. See “What is ETL?” dated 11 November 2022. As such the claim is not patent eligible.
Claims 2-11, 14, 15, 17-26, 29, 32-41, 44, and 46-49 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim(s) 2, 17, 32 merely describe(s) categorizing the data and determining recoverable appointments based on the categorizations, which further defines the abstract idea.
Claim(s) 3, 18, 33 merely describe(s) determining appointment parameters, which further defines the abstract idea.
Claim(s) 4, 19, 35 merely describe(s) determining a recovery factor, which further defines the abstract idea.
Claim(s) 5, 20, 36 merely describe(s) applying the recovery factor to determine recoverable appointments, which further defines the abstract idea.
Claim(s) 6, 21, 37 merely describe(s) applying a presorted reimbursement rate to the lost appointment data, which further defines the abstract idea.
Claim(s) 7, 22, 38 merely describe(s) determining a lost opportunity, which further defines the abstract idea.
Claim(s) 8, 23 merely describe(s) determining an access opportunity, which further defines the abstract idea.
Claim(s) 9, 24, 39 merely describe(s) categorizing lost appointments and determining a lost opportunity, which further defines the abstract idea.
Claim(s) 10, 25, 40 merely describe(s) the types of categories, determining a number of recovered appointments, and determining revenue associated with the recovered appointments, which further defines the abstract idea.
Claim(s) 11, 26, 41 merely describe(s) determining an access opportunity for the patient, which further defines the abstract idea.
Claim(s) 14, 29 merely describe(s) the tables, which further defines the abstract idea.
Claim(s) 15 merely describe(s) the data that is stored in the tables, which further defines the abstract idea.
Claim(s) 34 merely describe(s) appointment parameters, which further defines the abstract idea.
Claim(s) 44 merely describe(s) determining categories or data, which further defines the abstract idea.
Claim(s) 46, 47, 48 merely describe(s) outputting data and displaying it in one or more windows, which further defines the abstract idea. The claims further recite the additional elements of (nondescript) graphical elements, table elements, and panes which “generally link” the claimed invention to a particular technological environment or filed of use which cannot provide a practical application or significantly more in the same manner as the ETL additional element, supra.
Claim(s) 49 merely describe(s) the patient encounter data, which further defines the abstract idea.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-30, 32-41, 44, and 46-48, the Applicant has cancelled Claims 12, 13, 27, and 28, rendering the rejection of those claims moot. Regarding the remaining claims, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
The amended claims address the two deficiencies identified in the Final Office Action as the operative basis for the§ 101 rejection: (1) the finding that the machine learning (ML) models are undefined and encompass simplistic models that humans perform; and (2) the finding that the claimed data model recites only non-descript tables with non-functional labels that provide no concrete technical improvement.
Regarding (a), the Examiner respectfully submits that the machine learning models still encompass simplistic models that humans perform; logistic regression and decision trees are forms of supervised machine learning. The tables are part of the abstraction and remain so because only the type of data is described.
Notwithstanding this mandatory prohibition, the Final OA introduced three prior art references, namely, Almosni et al. (US 2013/0297536), Chamieh et al. (US 2019/0146970), and Halberstadt et al. (US 2016/0179630), at the Step 2A, Prong 2 analysis for the sole purpose of establishing that ETL is well-understood, routine, and conventional in the art.
Regarding (b), the Examiner respectfully submits that these references were introduced at Step 2B. See Final Rejection dated 11 February 2026 at numbered Pg. 8. At Step 2A2, ETL was found to generally link. This is true of the present rejection as well.
When properly evaluated without reference to conventionality, those additional elements, as amended to recite the specific six-pipeline architecture described in Specification paragraphs [0051]-[0053] and selected specific tables of the data model, reflect a concrete technical improvement to the processing of heterogeneous healthcare data, thus satisfying the improvement-to-technology consideration of MPEP §§ 2106.04(d)(l) and 2106.05(a), and integrating the abstract idea into a practical application at Step 2A, Prong 2 without proceeding further.
Regarding (c), the Examiner respectfully submits that the Applicant has not stated here why using ETL integrates the claimed invention into a practical application.
The Specification describes in detail the problems with conventional healthcare data processing: disparate, inconsistent data formats from multiple electronic health record systems (EHRs, HISs, RISs) that cannot be efficiently integrated, processed, or analyzed without the specific pipeline and data model architecture of the present invention. See Spec. ¶¶ [0006]-[0008].
Regarding (d), the Examiner respectfully submits that he has reviewed the Specification at the cited paragraphs and can find no mention of these purported problems. These cited paragraphs discuss problems with scheduling patients. Even assuming this was present, this is not a technical problem caused by the computer of the claim. Applicant’s is using ELT as a tool to collect data.
The Examiner's Final OA did not address these explicit specification disclosures at paragraphs [0095] and [0097] in either the Non-Final or Final OA.
Regarding (e), the Examiner respectfully submits that Applicant did not cite to these paragraphs in the prior response. If Applicant had, the Examiner would have pointed out that certain methods of organizing human activity does not exclude the use of a computer or multiple persons and that the Specification’s statement indicates that it may be performed manually—just that it is impractical. Regarding Spec. Para. 0097, a person having skill in the art would not understand the claims to be solving any technical problem. Boilerplate language at the end of the Specification that does not tie the actual problem to an actual solution is not persuasive.
The human mind is not equipped to perform the claimed pipeline transformations, including automated deduplication, temporal master file construction with ID remapping across multiple heterogeneous source systems, schema normalization with file size reduction, and sequential multi-stage data conveyance, any more than the human mind is equipped to detect suspicious network activity by analyzing network packets, which MPEP § 2106.04(a)(2)(11l)(A) identifies as a paradigmatic example of a claim that does not recite a mental process.
Regarding (f), the Examiner respectfully submits that this was not characterized as a mental process and thus this argument is irrelevant.
This disclosure [of machine learning models] expressly encompasses deep learning, reinforcement learning, and natural language processing models, which are now included in the amended claims, and none of which are simplistic mathematical models performable by a human using pencil and paper. […] This amendment ensures that the BRI of the ML model limitation cannot extend to simplistic mathematical models that could be performed by a human, foreclosing the Examiner's primary characterization of this limitation as a human-performable mental process.
Regarding (g), the Examiner respectfully submits that the machine learning models were claimed in the alternative along with supervised and unsupervised machine learning models. Again, the Examiner notes that supervised models include simplistic data analysis techniques such as logistic regression and decision trees. This is how the Applicant has defined the machine learning in the claim. Spec. Para. 0057 also states this.
The amended independent claims now recite that the one or more machine learning models: (a) comprise at least one of a supervised learning model….
Regarding (h), the Examiner respectfully submits that this fully supports the Examiner’s position.
The amended ML features are not a human mental process.
Regarding (i), the Examiner respectfully disagrees. Performing regression analysis and decision trees can most certainly be performed mentally (perhaps with the aid of pencil and paper). These features were also alternatively interpreted to be part of certain methods of organizing human activity.
Further, the amended features are an improvement to ML itself under Desjardins. The claimed multi-stage training process, in which a deep learning model is first trained on the full healthcare dataset with weights adjusted based on the complete data, then specifically tuned on a narrower dataset to perform a selected healthcare facility optimization task, is a specific, technically defined ML methodology that constitutes an improvement to how machine learning itself is implemented.
Regarding (j), the Examiner respectfully disagrees. Training and/or retaining of a machine learning model is not an improvement to the machine learning model per Recentive. There is no improvement to the model itself as in Dejardens. This is literally how machine learning normally operates.
The amended claims 1, 16, and 30 now expressly recite the specific six-pipeline architecture disclosed in Specification paragraphs [0051 ]-[0053]:…. This is not generic ETL.
Regarding (k), the Examiner respectfully disagrees. How the data is actually collected, transformed, and stored is part of the abstraction. The use of ETL to perform these functions is generally linking the claimed invention to a particular technological environment, ETL. The Examiner notes that Specification at Pg. 18 (PgPub para. 0051) states that a known, commercial ETL program may be used meaning that the ETL must be generic. Applicant is using ETL as a tool to perform its particular type of data ingestion.
The Specification confirms at paragraph [0051] that "the data pipelines, and particularly the specific data pipelines of the present invention, improve the processing capabilities of the underlying computing infrastructure and enhance the ability to process, store, and manipulate the data in real time."
Regarding (l), the Examiner respectfully disagrees. There is no nexus between the use of ETL to perform the abstract data collection, transformation, and storage and any improvement to “processing capabilities of the underlying computing infrastructure.” Put another way, there is no improvement to the physical aspects of the computer, nor would a person having skill in the art recognize such an improvement.
Further, Applicant respectfully notes that the Examiner's own prior art rejection acknowledged that the specific pipeline architecture recited in Claims 13 and 28 - the patient encounter standardization pipeline, temporal master file creation pipeline, final master file creation pipeline, file normalization pipeline, patient encounter final output pipeline, and provider schedule final output pipeline - were not taught by the prior art of record (Vegas Santiago, Brunel, or Gunawardena). This acknowledgment is directly relevant to Step 2B: elements that the prior art does not teach cannot, by definition, be well-understood, routine, and conventional as claimed.
Regarding (m), the Examiner respectfully submits that how the data is actually collected, transformed, and stored is part of the abstraction. Whether or not the abstraction is well-understood, routine, and convention is not part of the analysis. The tool used by the claim to perform that part of the abstraction—the ETL—is well-understood, routine, and convention.
This fine-tuning process modifies learned model weights and is a technically specific ML operation with no human-performable analog. Rather, it is precisely the type of operation that the Desjardins decision recognized as potentially conferring eligibility when tied to a defined technical function.
Regarding (n), the Examiner respectfully disagrees. This is how all machine learning occurs. The machine learning fits data by adjusting hyperparameters. This is not an improvement to machine learning. See Recentive where the CAFC indicated that training/retraining a machine learning model (which necessarily includes tuning parameters) is incident to the very nature of machine learning and does not provide an inventive concept (i.e., a practical application or significantly more).
Even if any individual element were viewed as insufficient standing alone, the ordered combination of the six-pipeline ETL architecture, the specific data model with defined tables, the analytical workflow for identifying and quantifying recoverable appointments, and the trained ML models together integrate the abstract idea into a concrete practical application.
Regarding (o), the Examiner respectfully disagrees. The tables and how the data is actually collected, transformed, and stored are part of the abstraction. As discussed previously, it is the additional elements that provide a practical application and the only additional elements are a computer and ETL. The combination of these additional elements does not provide a practical application when considered alone or in combination.
The amended independent claims now recite a data model comprising a healthcare-specific common data model with three structurally and functionally defined components:….
Regarding (p), the Examiner respectfully submits that these data tables do not improve the computer within the meaning of Enfish. Applicant has pointed to nothing that indicates that the computer is improved via the implementation of these tables. Applicant is storing and referencing data in tables in a particular manner; however, this is not analogous to the new type of self-referential data storage format of Enfish.
Under Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018), incorporated into MPEP § 2106.05(d)(I), a factual determination is required to support a WURC conclusion, and that determination must be expressly supported in writing with a citation to a court decision, a specification admission, a publication, or official notice.
Regarding (q), the Examiner respectfully submits that this is incorrect. As discussed previously, there is no requirement under Berkheimer that the rejection must find the abstraction to be well-understood, routine, and conventional. Only the additional elements that were found to represent extra-solution activity require Berkheimer evidence. The Examiner notes that he provided Berkheimer evidence for the ETL even though it was not required.
The Examiner's Final OA dismissed the ordered combination of the pipelines, data model with specific tables, and ML models in a single conclusory sentence without independently analyzing the combination as required. This failure constitutes a procedurally deficient WURC analysis under Berkheimer and MPEP § 2106.07(a)(III).
Regarding (r), as stated and then ignored by the Applicant “MPEP § 2106.05(d)(l)(3) provides that ‘even if one or more additional elements are well-understood, routine, conventional activity when considered individually, the combination of additional elements may amount to an inventive concept (emphasis added).’"
Elements that the Examiner has found to be novel and non-obvious over the prior art cannot simultaneously be "well-understood, routine, and conventional" for purposes of §101.
Regarding (s), the Examiner respectfully agrees and notes that the subject matter of Claim 45 is part of the abstraction. The abstraction does not require Berkheimer analysis as clearly indicated by the Applicant in argument (r). Put another way, MPEP 2106.05(I) states: “As made clear by the courts, the novelty of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter (internal quotations omitted, emphasis original).”
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Ginsberg et al. (U.S. Pre-Grant Patent Publication No. 2021/0110897) which discloses aggregating and tracking healthcare delivery to a patient including tracking missed appointments.
Chowdhry et al. (U.S. Pre-Grant Patent Publication No. 2021/0098133) which discloses a machine learning system for healthcare applications that provides a risk score based on a predictive model and which collects patient data via an ETL function.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON S TIEDEMAN whose telephone number is (571)272-4594. The examiner can normally be reached 7:00am-4:00pm, off alternate Fridays.
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/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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