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
In the amendment dated 15 December 2025, the following occurred: Claims 1, 4, 12, 16, 19, 25, 27, 30, 32, 39, 44, and 46 have been amended; Claims 31, 42, 43, and 45 have been cancelled.
Claims 1-30, 32-41, 44, and 46-48 are pending.
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 1-30, 32-41, 44, and 46-48 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, storing at least a portion of the extracted health data conveyed over one or more of the plurality of data pipelines in a data model to form stored health data, wherein the data model includes a plurality of tables for organizing and storing the extracted health data including the patient encounter data, lost appointment data, and schedule data, 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 trained on the patient encounter data, lost appointment data, and schedule data to form a trained machine learning model to the stored health data to generate predictions therefrom, 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 included as part of the abstract idea. A review of the Specification indicates that the particular type of model employed is not defined (see Spec. Pg. 23) and appears to encompass any type of machine learning including regression analysis and decision trees. 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 art in light of the disclosure would readily interpret the noted machine learning 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.
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 112(f) interpretation, supra), 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). Also, assuming arguendo that this is an additional element and not part of the abstract idea, the prior art of record indicates that ETL, which necessarily requires generation of pipelines, is well-understood, routine, and conventional in the field. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Claims 2-15, 17-29, and 31-48 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) 12, 27 merely describe(s) training and tuning (retraining) a model, which further defines the abstract idea. Claims 12 and 27 includes the additional element of the machine learning model being trained and tuned. The type of training/tuning utilized by the claimed invention is not described by the Applicant. As such the Examiner is required to analyze the training step given the broadest reasonable interpretation. The training of the ML is considered to be part of the abstract idea because they fall under data manipulations that humans perform and thus are part of the rules or instructions.
Claim(s) 13, 28 merely describe(s) the types of pipelines generated, which further defines the abstract idea.
Claim(s) 14, 29 merely describe(s) generating 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) 42 merely describe(s) determining an optimization opportunity, 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.
Response to Arguments
Drawings
Regarding the drawing objection(s), the Applicant has submitted replacement drawings which have alleviated the drawing issues. The drawings are accepted.
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-48, the Applicant has cancelled Claims 31, 42, 43, and 45, 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:
Applicant respectfully submits that the characterization of the current claims is overly broad and incorrect.
Regarding (a), the Examiner respectfully disagrees. The characterization of the claims reflecting a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, is not overly broad or incorrect.
The current claims recite generating data pipelines for transferring or conveying the health data and for storing the health data conveyed over the data pipelines in a data model. This claim language is not directed to a person following a set of rules and performing a series of acts (e.g., certain methods of organizing human activity), but rather are directed to specific digital steps that require the formation of discrete data pipelines for conveying selected types of health information and then storing the health information in a specially configured data model with selected tables for storing the selected types of health information.
Regarding (b), the Examiner respectfully disagrees. The recited “pipelines” are undefined and, given the broadest reasonable interpretation, represent taking raw information from one location and placing it in another location for storage and/or use. This represents steps that a person or persons would follow. For instance, every person who has attended school has read information from various sources (books), cleansed and interpreted the data (edited the information), and stored the cleansed/interpreted data in data locations (taken organized notes). Even assuming arguendo that this is not part of the abstract idea, the prior art of record indicates that ETL, which necessarily requires generation of pipelines, is well-understood, routine, and conventional in the field. The Applicant did not invent using data pipelines or ETL (see, e.g., Spec. Pg. 18 noting that the ETL of the invention may be performed using commercially available software) and is merely using these as a tool to collect data.
The claimed invention thus solves concrete technical problems in healthcare facility resource management through specialized computer-implemented processes including the creation of certain and discrete data pipelines and the storing of health data in a certain type of data model, namely, a data model configured for storing the selected types of health data. Specifically, the system addresses (1) converting disparate healthcare data sources into a unified data model with specialized tables for healthcare workflow optimization, (2) implementing specific data pipelines (e.g., six data pipelines) designed for healthcare encounter data processing, (3) applying machine learning models specifically trained on healthcare data to generate actionable predictions, and (4) providing technical solutions for appointment recovery and revenue optimization through categorization and recovery factor techniques. These are not abstract concepts but concrete technical implementations that improve computer functionality and improve the technical field of healthcare resource management.
Regarding (c), the Examiner respectfully disagrees and will address the arguments in turn. The applicant has not identified nor can the Examiner locate any technical problem that was cause by the technological environment to which the claim is confined (a generic computer) that the claimed invention is solving. Converting data (argument (1)) is not a problem caused by the computer. It is a problem that has existed since the creation of data. Similarly, collecting data from different sources (argument (2)) is also not a problem caused by the computer. Applying machine learning models (argument (3)) is also not a technical problem; it is a solution to a problem, but itself is not a technical problem. Finally, appointment recovery and revenue optimization (argument (4)) is also not a technical problem. This problem exists independently of the computer. Everything Applicant has identified is either a non-technical problem or is how the claim solves the non-technical problem.
The machine learning models are specifically trained on healthcare data including the patient encounter data, lost appointment data, and schedule data and are integral to the technical solution, not mere abstract mathematical concepts.
Regarding (d), the Examiner respectfully submits that there is nothing in the claims or specification that states what the machine learning models must or must not entail. The Examiner is thus obligated to give the phrase it’s broadest reasonable interpretation. This interpretation is that the claimed machine learning model may encompass simplistic types of data models that include linear/logistic regression and/or decision trees. These types of machine learning are types of models that humans perform and are thus interpreted to be part of the rules or instructions. This includes the “training” of these models as people routinely create (i.e., train) regression models and decision trees using data.
Applicant initially notes that the claims do not merely "link" the alleged abstract idea to a technical environment, but rather the technical environment is integrated into the core concept of the present invention.
Regarding (e), the Examiner respectfully submits that he is unclear what Applicant believes the distinction is. For instance, the technological environment in the claims in Alice Corp. were integrated into the core concept of the claims at issue as well.
As noted above, the specification clearly sets forth that the data pipelines employed by the system improve the processing capabilities of the underlying computing system. As such, the claim sets forth features that improve the function of a computing system. Consequently, the claimed system and method is incorporated into a practical application.
Regarding (f), the Examiner respectfully submits that there is no particularity at all with the “specific data pipelines” of the claim. The pipelines are functionally claimed to take data and store it in non-specific locations. Applicant is thus using a generic ETL for its intended purpose as both indicated by the prior art of record which indicates that ETL/pipelines are well-understood, routine and conventional and by the indication in the Specification at Pg. 18 that a known, commercial ETL program may be used. Further, a person having skill in the art would not understand this to provide any physical improvement to the computer; this is equivalent to arguing that Adobe Acrobat improves the computer because improves the computer’s ability to process PDFs.
Further, claim 1 (as an example claim) is directed to a specific, technologically-driven improvement in healthcare data processing systems, rather than the mere manipulation of data or performance of generic ETL routines.
Regarding (g), the Examiner respectfully submits that the Specification discloses that the ETL technique is performed by commercially available (i.e., generic) software. The prior art of record also indicates that utilization of ETL/pipeline functionality is well-understood, routine and conventional in the field. Further, there is no specificity with regard to how the ETL/pipeline supposedly operates.
The specification describes a healthcare-specific data model consisting a series of specialized tables (e.g., more than fifteen dedicated tables) that organize, structure, and store health data with far greater efficiency and reliability than conventional relational databases. See [¶0056].
Regarding (h), the Examiner respectfully submits that the claim does not recite “specialized tables.” The claims recite non-descript tables having non-functional labels. There is also no indication that these non-descript tables provide “far greater efficiency and reliability than conventional relational databases.” Spec. Para. 0056 (presumably of the associated PgPub 2024/0161915) does not describe any greater efficiency and reliability than conventional relational databases and merely lists the names of tables.
This specialized data model provides a concrete improvement to the computing system's ability to store and retrieve healthcare encounter information, analogous to the self-referential table in Enfish, which the Federal Circuit found to be an improvement to computer functionality itself rather than a mere abstract concept.
Regarding (i), the Examiner respectfully submits that Applicant has not claimed a specific data model that is even remotely analogous to that in Enfish.
The claims further recite precise computational mechanisms that cannot be performed as mental processes, such as categorizing appointments into "manageable" and "non-manageable" types using predefined rules (See ¶0059-0060]), computing recovery factors (See ¶0063]), determining optimization opportunities through detailed classifications of unavailable time (See ¶0078-0079]), and applying machine-learning models trained specifically on healthcare encounter data (¶0057). These specific rules and computational steps constitute unconventional technical solutions to computer-related problems, analogous to the rule-based animation improvements held eligible in McRO, and clearly extend beyond routine or abstract data processing.
Regarding (j), the Examiner respectfully disagrees. Taking these in turn, the claim was not characterized as a mental process, so this argument is immaterial. There is also no indication in the as-filed disclosure that computers could not previously be programmed to perform the claimed invention as in McRO.
This is akin to the technological improvements in Finjan, where the inventive arrangement of conventional components produced a practical and concrete enhancement to computer functionality.
Regarding (k), the Examiner respectfully disagrees. There is no unconventional arrangements of conventional components in the claim. The claim is confined to a general-purpose computer.
Further, the claimed invention improves the technical field of healthcare resource management.
Regarding (l), the Examiner respectfully disagrees. MPEP 2106.04(d)(1) states “the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” Here, there is no improvement to the computer nor is there an improvement to another technology because no other technology is recited in the claim. Because neither type of improvement is present in the claims, an improvement to technology is not present and there is no practical application.
Applicant’s argument that the field of healthcare resource management is a technology and the claimed invention improves this field is not reflected in the claimed invention. The claims are confined to a general-purpose computer and do not claim healthcare resource management. Moreover, the entire field of healthcare resource management is not reasonably understood to be a problem arising in technology, as it is instead a problem arising in healthcare. The claimed invention is using a computer as a tool and any improvement present is an improvement to the abstract idea of, to paraphrase, collect, analyze, store, and display information. Finally, where Applicant’s line of reasoning correct, the invention in Alice Corp. would have been subject matter eligible because it was an improvement to the technology of settlement risk mitigation.
Applicant further notes that the current claims are related to Example 42 of the Subject Matter Eligibility memorandum (July 2015)(PEG 42).
Regarding (m), the Examiner respectfully disagrees. MPEP 2106.04(d) sates that one way in which a claimed abstract idea may be subject matter eligible under prong 2A2 is if the claimed invention solves a described technological problem. Example 42 is an illustration of this. The Specification of Example 42 describes a technical problem (i.e., a problem caused by the technology): the technological implementation of software formats made it difficult to share updated health information. The claimed invention then solved this problem (a technical solution) by providing a message and access to updated real-time data that has been converted to a standardized format, thus integrating the abstract idea into a practical application. Unlike Example 42 and/or the technical solution to a technical problem inquiry, Applicant has not identified nor can the Examiner locate any technical problem that the claimed invention is solving. At best, the problem(s) described in the as-filed disclosure are medical/healthcare administrative problems.
Further, as in BASCOM Global Internet Services, Inc. v. AT&T Mobility LLC, the present claims recite an "unconventional and innovative arrangement" of known computing components that yields capabilities not previously attainable.
Regarding (m), the Examiner respectfully disagrees. The claims are confined to a general-purpose computer. There is no unconventional arrangement of anything recited in the claim.
Finally, the claims here are analogous to the claims upheld in DDR Holdings, LLC v. Hotels.com, L.P., where the Federal Circuit found eligibility because the claims addressed an **Internet-specific problem** using a technical, computer-implemented solution. The present claims solve healthcare-IT-specific problems relating to data fragmentation, resource-allocation inefficiency, and workflow bottlenecks, and do so by employing specialized data structures, coordinated pipelines, and machine-learning-driven determination units. The solution is therefore rooted in computer technology and specifically tailored to the healthcare information systems context, just as the solution in DDR was tailored to the challenges of Internet-based e-commerce.
Regarding (o), the Examiner respectfully disagrees and notes that the Applicant has provided no citation to the as-filed disclosure where these alleged problems are discussed. The Examiner cannot find any discussion of these alleged problems either. As such, they are unsupported conjecture on the part of Applicant’s representative, which cannot be persuasive.
The claimed invention includes a "non-conventional ordered combination" of elements that operates in a manner not found in routine healthcare IT systems.
Regarding (p), the Examiner respectfully submits that the test is not whether the elements of the claims (i.e., the abstract idea) are a "non-conventional ordered combination." Rather, this test is whether the ordered combination of additional elements provides significantly more. See MPEP 2106.05(I)(B). In Applicant’s claim, the only additional elements are a general-purpose computer and ETL (possibly including pipelines). The prior art of record indicates that there is nothing unconventional about this arrangement.
Moreover, under Berkheimer v. HP Inc., whether particular claim elements or their ordered combination are well-understood, routine, or conventional is a factual question that must be supported by evidence.
Regarding (q), the Examiner respectfully submits that this is incorrect. Berkheimer evidences is only required for additional elements that the Examiner previously found to represent extra-solution activity under step 2A2. See MPEP 2106.05(d)(I), MPEP 2106.07(a) (“At Step 2A Prong Two or Step 2B, there is no requirement for evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more than the exception unless the examiner asserts that additional limitations are well-understood, routine, conventional activities in Step 2B.”). The Examiner did not assert that any of the additional elements were extra-solution activity under step 2A2 and thus no evidence is required. For completeness, the Examiner did provide Berkheimer evidence that the additional element(s) of ETL/pipeline is well-understood, routine, and conventional in conjunction with generic computers.
The claims also improve the functioning of the underlying computer systems by enabling more efficient data organization, more accurate and streamlined pipeline processing, and improved analytics through coordinated application of machine learning and categorized datasets. This type of technological improvement is analogous to Visual Memory v. NVIDIA, where specialized data organization improved computer performance and was deemed patent-eligible.
Regarding (r), the Examiner respectfully submits that there is no claimed description of how the data is organized, how the pipelines operate, or how the machine learning is implemented. Applicant is not claiming anything remotely related to a memory system having programmable operational characteristics that are configurable based on the type of processor, which can be used with different types of processors without a tradeoff in processor performance as was claimed in Visual Memory.
Finally, Applicant also notes that the recent Desjardins decision issued by Director Squires directly bears upon this case.
Regarding (s), the Examiner respectfully submits that there is no improvement to machine learning as there was in the claims at issue in Desjardins. There is actually no disclosure of the specific type of machine learning the Applicant’s invention is using. See Spec. Pg. 23.
Rejection under 35 U.S.C. § 103
Regarding the rejection of Claims 1-48, the Applicant has cancelled Claims 31, 42, 43, and 45, rendering the rejection of those claims moot. Regarding the remaining claims, the Applicant has incorporated the subject matter of Claim 45 into the independent claims. The subject matter of Claim 45 was previously indicated as being over the prior art and, as such, the prior art rejection is withdrawn.
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:
Ghai et al. (U.S. Pre-Grant Patent Publication No. 2019/0356778) which discloses a healthcare appointment management system that uses an interactive voice response system to confirm patient appointments.
Pang et al. (U.S. Pre-Grant Patent Publication No. 2022/0293272) which discloses a machine learning system for providing predictions based on labeled data.
THIS ACTION IS MADE FINAL. 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.
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/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683