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 05/19/2026.
In the amendment dated 05/19/2026, the following occurred: Claims 1 and 9 were amended.
Claims 1-16 are currently 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/19/2026 has been entered.
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 1-16 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 and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recites a method and a system for developing, maintaining, and implementing a product formulary for medical product items, which are within a statutory category.
Step 2A1
Regarding claims 1 and 9, the limitation of (claim 1 being representative) determining a clinical usage hierarchy for a practice area, the clinical usage hierarchy including at least two organizational levels, where the first organizational level includes one or more organizational pass-through categories and where the second organizational level includes at least one substitutable group category; receiving one or more item entries for each substitutable group category included in the clinical usage hierarchy, wherein each of the one or more item entries includes at least item categorization information; training a […] using the received one or more item entries for each substitutable group category in the clinical usage hierarchy, wherein the […] is trained using items categorization information to identify items for inclusion in the substitutable group categories; deploying the […] populated with a plurality of item data entries, including item data entries distinct from the one or more item entries used to train the […], to identify a subset of item data entries of the plurality of item data entries for each substitutable group category included in the clinical usage hierarchy, where each item data entry of the plurality of item data entries includes at least one or more product categories, and the trained […] identifies the subset of item data entries for each substitutable group category based on at least the one or more product categories included in each item data entry of the subset of item data entries and the item categorization information in the one or more item data entries included in the respective substitutable group category; using metadata representing output of the trained […], detecting, by an […] an anomalous selection of an item data entry: in response to detecting the anomalous selection, generating by the […] an anomaly alert that triggers user review of the detected anomalous selection; storing the clinical usage hierarchy, each substitutable group category in the clinical usage hierarchy, and, in each substitutable group category, the one or more item entries and the identified subset of item data entries for the respective substitutable group category; and generating a product formulary for the practice area based on at least the stored clinical usage hierarchy, wherein an item associated with an item entry item data entry in each substitutable group category in the clinical usage hierarchy is substitutable with another item associated with an item entry item data entry in the same substitutable group category for a procedure associated with the practice area as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to determine clinical usage hierarchy, receive item entries, train a machine learning model, deploy the trained model, identify a subset of item data entries, detect an anomalous selection, generate an anomaly alert, store the clinical usage hierarchy, each substitutable group category, the one or more item entries and the identified subset and generate a product formulary in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “determining, receiving, training, deploying, identifying, detecting…generating…storing…and generating a product formulary” as indicated supra.
Other than reciting (in claim 1) a processor, a processing system, a receiver and a memory and (in claim 9) one or more processors, a non-transitory computer readable medium and a memory, the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). 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 – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements of a processor, a processing system, a receiver and a memory. Claim 9 recites the additional element of one or more processors, a non-transitory computer readable medium and a memory. These additional elements are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic server for enabling access to medical information or generic computer components for performing generic computer functions. Spec. at Para. [0030] states any suitable processing system can be used and [0084] teaches any suitable type of memory can be used. [0095] teaches processor can be a single processor, a plurality of processors, or combinations thereof. Processor devices can have one or more processor “cores.”) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 1 and 9 further recite the additional element of training a first machine learning model, a trained first machine learning model and an anomaly detection model. The Specification at Para. [0034] states training machine learning can be done manually. Utilizing trained machine learning model and the anomaly detection model equates to saying (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Claims 1 and 9 also recite the additional element of a universal item database. This additional element is recited at a high level of generality (i.e. a general means to receive/transmit/store data) and amounts to extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Step 2B
The claims do 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 element of the processor, processing system, receiver, memory, a non-transitory computer readable medium and one or more processors to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Moreover, using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Also as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of trained machine learning model and an anomaly detection model were determined to be “apply it”. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). Moreover, the additional element of a universal item database was considered extra-solution activity. This has been re-evaluated under “significantly more” analysis and determined to be well-understood, routine and conventional activity in the field. The prior art of record indicates that a database to store and access information from is well-understood, routine, conventional activity (see Tran at [0072], [0073], [0197] and Stevenson at [0005], [0023], [0025], [0042]). Therefore when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II));
Claims 2-8 and 10-16 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 and 10 further merely describe(s) identifying and populating a clinically relevant attribute hierarchy, training and deploying a second machine learning model and storing the populated clinically relevant attribute hierarchy. Claim(s) 2 and 10 also include the additional element of “a second machine learning model” and “a trained second machine learning model” which is analyzed in the same way as the first trained machine model above and does not provide practical application or significantly more for the same reason. Claim(s) 3 and 11 further merely describe(s) the universal item database. Claim(s) 3 and 11 include the additional element of “a Global Unique Device Identification Database (GUDID)” which is analyzed the same as the universal item database and does not provide practical application or significantly more for the same reason. Claim(s) 4 and 12 further merely describe(s) the at least one of the substitutable group categories and the one or more item entries and subset of item entries are organized into two or more graded tiers. Claim(s) 5 and 13 further merely describe(s) deploying a third machine learning model to organize the one or more item entries and subset of item data. Claim(s) 6 and 14 further merely describe(s) receiving a tier assignment and training the third machine learning model. Claim(s) 5, 6, 13 and 14 also include the additional element of “trained third machine learning model” which is analyzed in the same way as the first machine model above and does not provide practical application or significantly more for the same reason. Claim(s) 7 and 15 further merely describe(s) a user interface to display a plurality of form elements for entry of procedure data. Claim(s) 7 and 15 include the additional element of “user interface” which 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 and MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Claim(s) 8 and 16 further merely describe(s) an artificial intelligence engine configured to deploy the first trained machine learning model. Claim(s) 8 and 16 include the additional element of “an artificial intelligence engine” which is analyzed the same way as the first machine learning model and does not provide practical application or significantly more for the same reason.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of claims 1-16, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
Independent claim 1, as amended, does not merely recite organizing human activity, but instead recites a specific, computer-implemented process involving machine learning and automated anomaly detection. In particular, claim 1 requires: (i) training a machine learning model using categorized item entries associated with substitutable group categories, (ii) deploying the trained machine learning model on a universal item database that is distinct from the training data to identify additional item data entries, and (iii) using metadata from the trained machine learning model in a separate anomaly detection model to detect an anomalous selection and generate an alert that triggers user review. Applicant respectfully submits that these features cannot reasonably be characterized as "managing personal behavior" or "interactions between people." Rather, these features define a technical data processing pipeline involving multiple computational models operating on structured datasets. The Office's analysis removes these technological features and instead reduces the claim to a generalized description of "determining," "receiving," "identifying," and "generating," which Applicant respectfully submits is an improper abstraction.
Regarding 1, The Examiner respectfully disagrees. Under the broadest reasonable interpretation, claim 1 covers certain methods organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components. A person can follow a set of rules or instructions to perform the abstract idea of determining a clinical usage hierarchy, receiving one or more item entries, training using the received one or more item entries, identify a subset of item data entries, detecting an anomalous selection, generating an anomaly alert, storing the clinical usage hierarchy, each substitutable group category, and the one or more item entries and the identified subset of item data entries and generating a product formulary for the practice area in the manner described in the identified abstract idea, supra. The claim does not recites a specific, computer-implemented process involving machine learning but instead applies known machine learning techniques to process data. Moreover, detecting anomaly is an abstract idea and this indeed falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The Office further asserts that the claims recite an abstract idea "but for the recitation of generic computer components." Applicant respectfully contests this assertion. Applicant's claims do not merely append generic computer components to an otherwise abstract idea but instead recite a specific configuration and use of those components to perform a non- conventional, computer-implemented process. In particular, the claimed processor and processing system are not used in a generic manner to perform basic data storage or retrieval. Rather, they are specifically configured to (i) train a machine learning model using categorized item entries associated with substitutable group categories, (ii) deploy the trained machine learning model on a universal item database that is distinct from the training data to identify additional item data entries, and (iii) provide metadata from the trained machine learning model to a separate anomaly detection model. The anomaly detection model, in turn, detects an anomalous selection of an item data entry and generates an alert that triggers user review. These operations reflect a specific, ordered interaction between multiple computational components, including at least two distinct models, and are not generic uses of a computer.
Regarding 2, The Examiner respectfully disagrees. The claims use generic computer components to perform the abstract idea. The additional elements of a processor, a processing system, a receiver, a memory, one or more processors and a non-transitory computer readable medium are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic server or computer component for enabling access to medical information or for performing generic computer functions). Applicant’s Spec. at Para. [0030] states any suitable processing system can be used and [0084] teaches any suitable type of memory can be used. [0095] teaches processor can be a single processor, a plurality of processors, or combinations thereof. Processor devices can have one or more processor “cores.”. This amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
See, e.g., BASCOM Global Internet Services, Inc. v. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016) (holding that an inventive concept can exist in the non-conventional and non-generic arrangement of known, conventional computer components). Here, the claimed invention requires a particular arrangement in which a first machine learning model produces outputs that are then used as metadata by a separate anomaly detection model to detect unusual selections and trigger review. This inter-model dependency and processing pipeline goes well beyond generic data processing.
Regarding 3, the Examiner respectfully disagrees. Applicant’s invention is unlike that of BASCOM Glob. Internet Servs. v. AT&T Mobility LLC. Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. Because this approach considers all claim elements, the Supreme Court has noted that "it is consistent with the general rule that patent claims ‘must be considered as a whole.’
Whether considered separately or as a whole, Applicant’s claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of a processor, a processing system, a receiver, a memory, one or more processors and a non-transitory computer readable medium to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
The Office's analysis focuses on whether individual components (e.g., processor, memory) are generic in isolation. Its approach evaluated the claims at an impermissibly high level of generality and failed to consider how the claims recite a specific, ordered combination of elements that together provide a practical application. The proper inquiry under Step 2A, Prong Two is not whether individual elements are generic, but whether the claim as a whole integrates any alleged abstract idea into a practical application. See, e.g., MPEP §2106.04(d).
Regarding 4, the Examiner respectfully disagrees. Whether considered alone or in combination, the additional elements of a processor, a processing system, a receiver, a memory, one or more processors and a non-transitory computer readable medium do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Desjardins was designated precedential on November 4, 2025. The related MPEP Update took effect December 5, 2025. Desjardins establishes standards of analysis directly applicable here: (1) claims must not be evaluated at a high level of generality; (2) additional elements must not be dismissed as generic computer components without considering whether they confer a technological improvement to a technical problem; and (3) the specification's identification of improvements supports a finding that the claim reflects those improvements, even if not explicitly recited in claim language.
Regarding 5, The Examiner respectfully notes that Applicants disclosure supports that the additional elements are generic computer components. Moreover, Applicants disclosure aims to facilitate an efficient process for obtaining medical product items (e.g., physician preference items) in compliance with all applicable regulations, and provide a formulary for medical products that can serve the needs of physician or healthcare providers to obtain products that can ensure proper clinical care as well as compliance with all applicable rules and regulations, which is not a technical problem/solution.
The Office asserts that the machine learning model and anomaly detection model merely amount to "apply it" language. Applicant respectfully disagrees and submits that the claims do not merely state "apply machine learning," but instead specify how the machine learning model is trained, how it is deployed on a distinct dataset, and how metadata from that model is used by a separate anomaly detection model. This interdependence between models, including the use of metadata from one model as input to another, constitutes a specific technological implementation and not a generic instruction to apply an abstract idea. The Office further asserts that the "universal item database" is a generic data storage element and amounts to extra-solution activity. However, Applicant' claims require that the trained machine learning model is deployed on a universal item database that is distinct from the training data, which reflects a specific architectural feature enabling cross-dataset application of a trained model. This is not a generic storage function, but a functional requirement that enables the system to extend learned categorizations to new data and dynamically identify additional items. Such use of a separate dataset is integral to the claimed operation and cannot be dismissed as extra-solution activity.
Regarding 6, The Examiner respectfully submits that the additional element of training a using and training a first machine learning model and an anomaly detection model equates to saying (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. The Specification at Para. [0034] states training machine learning can be done manually. Moreover, the additional element of a universal item database is recited at a high level of generality (i.e. a general means to receive/transmit/store data), this represents a location to which data is stored and accessed and amounts to extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Applicant respectfully submits that the specification describes multiple specific technical improvements to computer-implemented systems for generating and maintaining medical product formularies, which are reflected in the amended claims. First, Applicant's specification explains that conventional approaches to identifying medical product items for inclusion in a formulary are limited and inefficient… For example, the specification explains that a machine learning model is trained using sample data for known products and is then deployed to identify additional items that should be included in those categories… Second, paragraph [0034] of Applicant's specification describes deploying the trained machine learning model on a universal item database to identify relevant items beyond those used for training. This enables the system to apply learned categorizations to a broader dataset and dynamically expand substitutable group categories. This represents a technical improvement in data processing by enabling a trained machine learning model to be applied to a universal item database to identify additional item data entries for inclusion in substitutable group categories. Third, paragraph [0034] of the specification discloses the use of metadata from a trained machine learning model in an anomaly detection process to identify unusual or inconsistent selections. Specifically, Applicant's specification explains that metadata from the machine learning model can be used by an anomaly detection model to detect an unusual selection of an item and generate an alert for user review. This provides a technical improvement by enabling detection of unusual selections of item data entries using an anomaly detection model that operates on metadata from a trained machine learning model and generates an alert that triggers user review. Fourth, paragraph [0034] of the specification describes generating an alert in response to detecting an anomalous selection to trigger user review. This feature provides a targeted intervention mechanism, ensuring that user attention is directed only to selections that deviate from expected patterns, thereby improving efficiency and reducing unnecessary manual review.
Regarding 7, the Examiner disagrees. The claims apply known machine learning and training techniques to identify relevant items, this cannot provide an improvement to machine learning technology as machine learning technology is not being improved upon but rather applied. Moreover, none of the above improvements the Applicant refers to are technical improvement as none of them improve upon the functioning of a computer not to a technical field. Identifying items for inclusion, relevant items, additional item data entries for inclusion, identify unusual or inconsistent selections, detect an unusual selection of an item and generate an alert for user review are all abstract and cannot render technical improvements. Furthermore, Applicants disclosure does not provide technical/computer related improvements. As an example, the specification at para. [0070] states improvement to the selection process for items for a procedure which is not a technical improvement.
Given the strength of Applicant's positions under Step 2A, Prongs One and Two of the 2019 Revised Guidance and the October 2019 update, Applicant respectfully submits that further analysis of the claims under Step 2B is not warranted. Namely, Applicant believes that just as the claim elements integrate the alleged judicial exception into a practical application, these same elements recite significantly more and provide a technical advancement over known methods and/or systems.
Regarding 8, the Examiner respectfully disagrees. Applicants claims recite an abstract idea implemented by generic computer components. There is no practical application nor significantly more.
Rejection under 35 U.S.C. § 103
Regarding the rejection of claims 1-16, the Examiner has considered the Applicant’s arguments, and finds them persuasive. The cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within the independent claim. In particular, the cited prior art of record fails to expressly teach or suggest the combination of: determining, by a processor of a processing system, a clinical usage hierarchy for a practice area, the clinical usage hierarchy including at least two organizational levels, where the first organizational level includes one or more organizational pass-through categories and where the second organizational level includes at least one substitutable group category; receiving, by a receiver of the processing system, one or more item entries for each substitutable group category included in the clinical usage hierarchy, wherein each of the one or more item entries includes at least item categorization information; training, by the processor of the processing system, a first machine learning model using the received one or more item entries for each substitutable group category in the clinical usage hierarchy, wherein the first machine learning model is trained using the item categorization information to identify items for inclusion in the substitutable group categories; deploying, by the processing system, the trained first machine learning model on a universal item database populated with a plurality of item data entries, the universal item database including item data entries distinct from the one or more item entries used to train the first machine learning model, to identify a subset of item data entries of the plurality of item data entries for each substitutable group category included in the clinical usage hierarchy, where each item data entry of the plurality of item data entries includes at least one or more product categories, and the trained first machine learning model identifies the subset of item data entries for each substitutable group category based on at least the one or more product categories included in each item data entry of the subset of item data entries and the item categorization information in the one or more item data entries included in the respective substitutable group category; using metadata representing output of the trained first machine learning model, detecting, by an anomaly detection model, deployed by the processing system, an anomalous selection of an item data entry from the universal item database; in response to detecting the anomalous selection, generating, by the anomaly detection model, an anomaly alert that triggers user review of the detected anomalous selection; storing, in a memory of the processing system, the clinical usage hierarchy, each substitutable group category in the clinical usage hierarchy, and, in each substitutable group category, the one or more item entries and the identified subset of item data entries for the respective substitutable group category; and generating, by the processor of the processing system, a product formulary for the practice area based on at least the stored clinical usage hierarchy, wherein an item associated with an item entry item data entry in each substitutable group category in the clinical usage hierarchy is substitutable with another item associated with an item entry item data entry in the same substitutable group category for a procedure associated with the practice area.
Conclusion
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Stangel (US 2017/0068787) teaches medical artificial intelligence system. Epstein (US 2014/0330588) teaches system and method for clinical strategy for therapeutic pharmacies.
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/LIZA TONY KANAAN/Examiner, Art Unit 3683
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