DETAILED ACTION
Status of Claims
The following is a final office action in response to the amendment filed January 5, 2026. Claim 1 is currently pending and have been examined.
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 .
Response to Arguments
Applicant argues that amended claim 1 is patent-eligible under §101. The Examiner respectfully disagrees.
Step 2A, Prong One – The Claim Recites an Abstract Idea
Applicant asserts that amended claim 1 merely “involves” an abstract idea rather than “reciting” one, because the claim does not explicitly set forth mathematical formulas and because machine learning models allegedly cannot be performed in the human mind. This argument is unpersuasive.
1. Mathematical Concepts Need Not Be Expressed as Formulas
While the Kim Memo clarifies that claims which merely involve mathematical concepts are not abstract, amended claim 1 recites mathematical concepts by expressly requiring:
a predictive machine learning model trained with patient data,
an artificial neural network trained on parameters, and
outputs of those models used to determine patient lifecycle events and healthcare service selections.
Under MPEP § 2106.04(a), mathematical concepts are not limited to explicit equations or formulas. Algorithms, predictive models, and neural networks are mathematical constructs even when described functionally. As clarified by Federal Circuit precedent (e.g., SAP v. InvestPic, Electric Power Group), reciting advanced statistical or predictive techniques at a high level constitutes recitation of a mathematical concept even absent explicit equations.
Accordingly, amended claim 1 recites, rather than merely involves, mathematical concepts.
2. Mental Processes Are Not Excluded Simply Because Automation Is Complex
Applicant further argues that the claim does not recite a mental process because “no human is capable” of applying trained machine learning models. This argument misapplies the Kim Memo.
The proper inquiry is whether the claimed steps reflect concepts that can be practically performed in the human mind, not whether a human can execute them at machine speed or scale. The claim recites, at a conceptual level:
identifying a patient lifecycle event based on patient information,
selecting healthcare services based on disease, location, expertise, procedure, hospital, and pricing,
determining prices for selected services, and
allocating payment among providers.
These steps describe information evaluation, selection, and financial allocation, which are classic mental processes and methods of organizing human activity, even if the claim specifies that they are performed using machine learning models. As explained in MPEP § 2106.04(a)(2), automation of mental processes using computers does not remove the abstract nature of the underlying idea.
Thus, the claim recites abstract ideas in the form of:
mental processes,
mathematical concepts, and
methods of organizing human activity (healthcare coordination and payment).
Step 2A, Prong Two – No Integration Into a Practical Application
Applicant alternatively argues that the claim integrates any abstract idea into a practical application by using “specially-trained” AI models to identify healthcare events and services. This argument is not persuasive.
1. “Specially-Trained” Does Not Equal Technical Improvement
The claim does not specify:
how the predictive model is trained,
how the neural network differs from conventional models,
any particular model architecture,
any improvement to computer performance, memory usage, training efficiency, or system reliability.
Merely labeling models as “specially-trained” does not meaningfully limit the claim or reflect an improvement to computing technology itself. As consistently held by the Federal Circuit, use of generic machine learning techniques to analyze data and make decisions within a business or administrative workflow does not integrate an abstract idea into a practical application.
2. The Claim Implements, Rather Than Improves, a Healthcare Workflow
The claim’s alleged “practical effect” is enabling patients to obtain and pay for healthcare services more efficiently. This benefit reflects an improvement to a business or administrative process, not to the operation of a computer or technical system.
The additional elements (database, processor, user interface, machine learning algorithms) are used in their ordinary and expected manner to:
store data,
analyze information,
present results, and
execute payments.
Such use does not impose a meaningful limit on the abstract idea, but instead represents applying the abstract idea using generic computer technology, which MPEP § 2106.04(d) expressly identifies as insufficient for patent eligibility.
Step 2B – No Inventive Concept
Finally, even if the claim were considered at Step 2B, it does not recite an inventive concept.
All additional elements—machine learning models, neural networks, databases, processors, EHRs, pricing records, and payment distribution—are well-understood, routine, and conventional in the healthcare IT field. The claim merely combines these known elements to perform their expected functions in a predictable manner.
No element or combination of elements transforms the abstract idea into a patent-eligible application.
Applicant's prior art arguments have been considered but are moot in view of the new ground(s) of rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 is are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Step 1: Statutory Category
(MPEP § 2106)
The claim is directed to a method comprising steps of receiving service selections, determining pricing, and distributing payment among healthcare providers. Therefore, it falls within the statutory category of a “process” as set forth under 35 U.S.C. § 101.
Conclusion: The claim is directed to a statutory category (process).
Step 2A, Prong One: Judicial Exception – Abstract Idea
(MPEP § 2106.04(a))
The claim is directed to the organization of financial transactions for healthcare services, which involves:
Collecting information (service selections),
Performing calculations (determining total price based on provider fees), and
Executing financial transactions (distributing payments).
This falls under the abstract idea grouping of:
“Certain methods of organizing human activity”, specifically commercial interactions, including sales activities, pricing, and payment distribution (MPEP § 2106.04(a)(2)(ii)).
It also involves “mental processes”, such as performing mathematical calculations (determining price allocation) and organizing payments, which could theoretically be done manually (MPEP § 2106.04(a)(2)(iii)).
Conclusion: The claim recites a judicial exception — an abstract idea involving pricing, financial transactions, and commercial interaction management.
Step 2A, Prong Two: Integration into a Practical Application
(MPEP § 2106.04(d))
The claim includes:
Use of a processor to receive selections, determine prices, and distribute payments.
Evaluation Under MPEP Considerations:
The use of a processor is a generic computer component executing standard data processing tasks.
The claim does not improve the processor itself, any network, or data storage — there is no enhancement to computer functionality (MPEP § 2106.05(a)).
The claim is not tied to a specific machine beyond the generic use of a processor (MPEP § 2106.05(b)).
The steps do not involve a transformation of an article into a different state or thing (MPEP § 2106.05(c)); distributing payments and calculating pricing are financial operations, not physical transformations.
The claim merely automates a conventional financial and administrative process — determining a price and paying multiple providers — without offering a technological solution (MPEP § 2106.05(f)).
Conclusion: The claim applies the abstract idea in the context of healthcare pricing and payment but does not integrate the abstract idea into a practical application beyond generic computing.
Step 2B: Inventive Concept
(MPEP § 2106.05 and § 2106.07(a))
Are there additional elements that amount to “significantly more” than the abstract idea?
The use of a processor to perform well-known functions like receiving input, performing price calculations, and distributing payments is generic and conventional.
There is no recitation of specialized hardware, improved data structures, or an unconventional technical solution.
The operations described are routine administrative functions that are common in service-based industries — e.g., bundling services, determining pricing based on component fees, and distributing payments among multiple parties.
WURC (Well-Understood, Routine, Conventional) Analysis:
These are standard operations in commerce and are not technically novel.
As supported by:
The specification, which does not describe technical improvements to computing systems.
Prior case law (Alice, Versata, Smart Systems) that confirms financial and pricing operations are abstract when implemented on generic computing components.
Conclusion: The claim does not recite an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
Therefore, the claim is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Wizig (US 8,494,881 B1) in view of Canton (US 2014/0365240 A1).
Regarding claim 1, Wizig discloses a method comprising:
receiving a digital indication comprising a selection of a plurality of healthcare services associated with a respective plurality of providers, using a processor (Wizig: Figure 30, column 5 lines 25-30 - is a diagram illustrating a comprehensive selection registration form.);
determining a price for the selected plurality of healthcare services based on an information record comprising an individual fee for each provider of the plurality of providers to respectively perform the selected plurality of healthcare services at a service location, using the processor; and (Wizig: Figure 30, column 5 lines 25-30 - is a diagram illustrating a comprehensive selection registration form, copayment);
distributing payment based on the price allocated among the plurality of providers, using the processor (Wizig: Figure 32 - checkout).
Wizig does not expressly disclose storing, in a database via a machine learning algorithm, each healthcare service of a plurality of healthcare services associated with a respective plurality of providers; graphically presenting, to a user via the machine learning algorithm, a selection of healthcare services of the plurality of healthcare services, wherein the selection is determined by the machine learning algorithm by applying an artificial neural network trained on parameters comprising one or more of: a type of disease, a location, an expertise, a procedure, a hospital, and a pricing requirement; wherein:the plurality of healthcare services is determined by the processor in response to a patient lifecycle event determined for a patient based on an electronic health record, and the patient lifecycle event determination is based on an output of a predictive machine learning model trained with patient data. Canton discloses:
storing, in a database via a machine learning algorithm, each healthcare service of a plurality of healthcare services associated with a respective plurality of providers (Canton: paragraph [0063] - The automated mobile self-help system can capture a consumer's personalized disease state and/or prescription history. In this way, this information can be viewed on the telehealth service system website and/or with a mobile application. This information can be integrated into a consumer's personal digital health record. Integrated laboratory results and other healthcare tests into our proprietary member portal through the web or mobile application that can be populated by predictive analytics);
graphically presenting, to a user via the machine learning algorithm, a selection of healthcare services of the plurality of healthcare services, wherein the selection is determined by the machine learning algorithm by applying an artificial neural network trained on parameters comprising one or more of: a type of disease, a location, an expertise, a procedure, a hospital, and a pricing requirement (Canton: Figure 5, paragraph [0063] - Process 500 (an in some embodiments, the systems of FIGS. 1-4) can utilize machine-learning algorithms. Example machine-learning algorithms, such support vector machines (SVM), can include statistical classification analysis algorithms, statistical regression analysis algorithms, and the like. For example, discovery unit 124 can include an SVM module (not shown). SVM module can supervise learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The SVM module can take a set of input data and predict, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier);
wherein:the plurality of healthcare services is determined by the processor in response to a patient lifecycle event determined for a patient based on an electronic health record, and (Canton: paragraph [0063] - The automated mobile self-help system can capture a consumer's personalized disease state and/or prescription history. In this way, this information can be viewed on the telehealth service system website and/or with a mobile application. This information can be integrated into a consumer's personal digital health record. Integrated laboratory results and other healthcare tests into our proprietary member portal through the web or mobile application that can be populated by predictive analytics);
the patient lifecycle event determination is based on an output of a predictive machine learning model trained with patient data (Canton: paragraph [0029] - Data analysis can include inspecting, cleaning, transforming, and modeling data by data mining, business, exploratory data analysis (EDA), confirmatory data analysis (CDA), text analytics and data modeling. Predictive analysis can use statistical modeling to analyze existing data to make predictions about future events).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and apparatus of Hess to have included storing, in a database via a machine learning algorithm, each healthcare service of a plurality of healthcare services associated with a respective plurality of providers; graphically presenting, to a user via the machine learning algorithm, a selection of healthcare services of the plurality of healthcare services, wherein the selection is determined by the machine learning algorithm by applying an artificial neural network trained on parameters comprising one or more of: a type of disease, a location, an expertise, a procedure, a hospital, and a pricing requirement; wherein: the plurality of healthcare services is determined by the processor in response to a patient lifecycle event determined for a patient based on an electronic health record, and the patient lifecycle event determination is based on an output of a predictive machine learning model trained with patient data, as taught by Canton because it would optimize the transactional experience (Canton: paragraph [0007]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHLEEN G PALAVECINO whose telephone number is (571)270-1355. The examiner can normally be reached M-F 9-4.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Smith can be reached at (571) 272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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KATHLEEN GAGE PALAVECINO
Primary Examiner
Art Unit 3688
/KATHLEEN PALAVECINO/ Primary Examiner, Art Unit 3688